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Public Health Public Health

On the prediction of isolation, release, and decease states for COVID-19 patients: A case study in South Korea.

In ISA transactions

A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting tens of millions people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The prediction can help health providers and decision makers to distinguish the states of infected patients based on their features in early intervention to take an action either by releasing or isolating the patient after the infection. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis of DS4C dataset is also provided. Experimental results and evaluation show that multinomial logistic regression outperforms other approaches with 95% in a state prediction accuracy and a weighted average F1-score of 95%.

Alafif Tarik, Alotaibi Reem, Albassam Ayman, Almudhayyani Abdulelah


COVID-19, Classification, Decease, Decision tree, Isolation, Multinomial logistic regression, Prediction, Random forest, Release

General General

Machine Learning Model For Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

In IEEE journal of biomedical and health informatics

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.

Serra Ginalber L O, Gomes Daiana Caroline Dos Santos


General General

Distant Domain Transfer Learning for Medical Imaging.

In IEEE journal of biomedical and health informatics

Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. However, conventional deep learning has two major drawbacks: 1) insufficient training data and 2) the domain mismatch between the training data and the testing data. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method to a recent issue (Coronavirus Diagnose). Several studies indicate that lung Computed Tomography (CT) images can be used for a fast and accurate COVID-19 diagnosis. However, well-labeled training data sets cannot be easily accessed due to the novelty of the disease and the privacy policies. The proposed method has two components: Reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnose using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.

Niu Shuteng, Liu Meryl, Liu Yongxin, Wang Jian, Song Houbing


General General

Learning the language of viral evolution and escape.

In Science (New York, N.Y.)

The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence's grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution.

Hie Brian, Zhong Ellen D, Berger Bonnie, Bryson Bryan


Public Health Public Health

Patient Journey Map to Improve the Home Isolation Experience of Persons with Mild COVID-19 Symptoms: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : In the context of COVID-19 outbreak, 80% of the persons are those with mild symptoms who are required to self-recover at home. They have a strong demand for remote healthcare that despite the great potential of artificial intelligence are not met in the current (e)-health services. Understanding the real needs of these persons is lacking.

OBJECTIVE : The aim of this paper is to contribute with a fine grained understanding of the home isolation experience of persons with mild COVID-19 symptoms, in order to enhance AI in eHealth services.

METHODS : Design research in which a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from top viewed personal video stories on YouTube and their additional comment threads. For the analysis this data was transcribed, coded and mapped into the patient journey map.

RESULTS : The key findings on the home isolation experience of persons with mild COVID-19 symptoms concern: (a) Considerable awareness period before testing positive and home-recovery period; (b) Less generic but more personal symptoms experiences; (c) Negative mood experience curve; (d) Inadequate home healthcare service support for mild COVID-19 patients through all stages. (e) Benefits and drawbacks of Social media support for mild COVID-19 patients; (f) Several touchpoint needs for home healthcare interaction with AI.

CONCLUSIONS : The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves Health - and IT professionals to more effectively apply AI technology into eHealth services for mild Covid-19 patients, for which three main service concepts are proposed: (I) Trustful public health information to release stress; (II) Personal Covid-19 health monitoring. (III) Community Support.

He Qian, Du Fei, Simonse Lianne W L


General General

Predicting the intention to use mobile learning during Coronavirus Pandemic through Machine Learning Algorithms.

In JMIR medical education

BACKGROUND : Mobile learning has become an essential instruction platform in many schools, colleges, universities and various educational institutions across the globe as a result of the COVID-19 pandemic crises. The resulting severe pandemic circumstances disrupted physical and face-to-face or contact teaching practices requiring many students to use mobile technologies actively for learning. M-learning offers a viable online teaching and learning platform that is accessible to teachers and learners globally.

OBJECTIVE : This paper investigates the use of mobile learning platforms for instruction purposes in UAE higher education institutions.

METHODS : An extended technology acceptance model (TAM) and theory of planned behavior (TPB) were proposed to analyze the adoption of mobile learning platforms by university students for accessing course materials, searching the web for information related to their disciplines, sharing knowledge and submitting assignments during the COVID-19 pandemic. The total number of questionnaires collected was 1880 from different universities in the UAE. Partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms (ML) were utilized to investigate the research model based on the data gathered through a student survey.

RESULTS : From the results, each hypothesized relationship within the research model was supported by the data analysis methods. It should also be noted that the J48 classifier mostly had the upper hand on other classifiers (89.37% accuracy), when it came to the prediction of the dependent variable.

CONCLUSIONS : The research revealed that teaching and learning could significantly benefit from the adoption of remote learning systems as an educational tool during the COVID-19 pandemic. However, its value could be lessened because of emotions students experience including fear of poor grades, stressful family circumstances and loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during this pandemic.


Salloum Th Said Abdelrahim, Akour Iman, Alshurideh Nd Muhammad, Al Kurdi Rd Barween, Al Ali Th Amal


General General

Federated Learning used for predicting outcomes in SARS-COV-2 patients.

In Research square

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Flores Mona, Dayan Ittai, Roth Holger, Zhong Aoxiao, Harouni Ahmed, Gentili Amilcare, Abidin Anas, Liu Andrew, Costa Anthony, Wood Bradford, Tsai Chien-Sung, Wang Chih-Hung, Hsu Chun-Nan, Lee C K, Ruan Colleen, Xu Daguang, Wu Dufan, Huang Eddie, Kitamura Felipe, Lacey Griffin, Corradi Gustavo César de Antônio, Shin Hao-Hsin, Obinata Hirofumi, Ren Hui, Crane Jason, Tetreault Jesse, Guan Jiahui, Garrett John, Park Jung Gil, Dreyer Keith, Juluru Krishna, Kersten Kristopher, Rockenbach Marcio Aloisio Bezerra Cavalcanti, Linguraru Marius, Haider Masoom, AbdelMaseeh Meena, Rieke Nicola, Damasceno Pablo, Silva Pedro Mario Cruz E, Wang Pochuan, Xu Sheng, Kawano Shuichi, Sriswa Sira, Park Soo Young, Grist Thomas, Buch Varun, Jantarabenjakul Watsamon, Wang Weichung, Tak Won Young, Li Xiang, Lin Xihong, Kwon Fred, Gilbert Fiona, Kaggie Josh, Li Quanzheng, Quraini Abood, Feng Andrew, Priest Andrew, Turkbey Baris, Glicksberg Benjamin, Bizzo Bernardo, Kim Byung Seok, Tor-Diez Carlos, Lee Chia-Cheng, Hsu Chia-Jung, Lin Chin, Lai Chiu-Ling, Hess Christopher, Compas Colin, Bhatia Deepi, Oermann Eric, Leibovitz Evan, Sasaki Hisashi, Mori Hitoshi, Yang Isaac, Sohn Jae Ho, Murthy Krishna Nand Keshava, Fu Li-Chen, de Mendonça Matheus Ribeiro Furtado, Fralick Mike, Kang Min Kyu, Adil Mohammad, Gangai Natalie, Vateekul Peerapon, Elnajjar Pierre, Hickman Sarah, Majumdar Sharmila, McLeod Shelley, Reed Sheridan, Graf Stefan, Harmon Stephanie, Kodama Tatsuya, Puthanakit Thanyawee, Mazzulli Tony, Lavor Vitor de Lima, Rakvongthai Yothin, Lee Yu Rim, Wen Yuhong


General General

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

In Scientific reports ; h5-index 158.0

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Lopez-Rincon Alejandro, Tonda Alberto, Mendoza-Maldonado Lucero, Mulders Daphne G J C, Molenkamp Richard, Perez-Romero Carmina A, Claassen Eric, Garssen Johan, Kraneveld Aletta D


Radiology Radiology

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

In Scientific reports ; h5-index 158.0

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

Ebrahimian Shadi, Homayounieh Fatemeh, Rockenbach Marcio A B C, Putha Preetham, Raj Tarun, Dayan Ittai, Bizzo Bernardo C, Buch Varun, Wu Dufan, Kim Kyungsang, Li Quanzheng, Digumarthy Subba R, Kalra Mannudeep K


General General

Fighting viruses with materials science: Prospects for antivirus surfaces, drug delivery systems and artificial intelligence.

In Dental materials : official publication of the Academy of Dental Materials

OBJECTIVE : Viruses on environmental surfaces, in saliva and other body fluids represent risk of contamination for general population and healthcare professionals. The development of vaccines and medicines is costly and time consuming. Thus, the development of novel materials and technologies to decrease viral availability, viability, infectivity, and to improve therapeutic outcomes can positively impact the prevention and treatment of viral diseases.

METHODS : Herein, we discuss (a) interaction mechanisms between viruses and materials, (b) novel strategies to develop materials with antiviral properties and oral antiviral delivery systems, and (c) the potential of artificial intelligence to design and optimize preventive measures and therapeutic regimen.

RESULTS : The mechanisms of viral adsorption on surfaces are well characterized but no major breakthrough has become clinically available. Materials with fine-tuned physical and chemical properties have the potential to compromise viral availability and stability. Emerging strategies using oral antiviral delivery systems and artificial intelligence can decrease infectivity and improve antiviral therapies.

SIGNIFICANCE : Emerging viral infections are concerning due to risk of mortality, as well as psychological and economic impacts. Materials science emerges for the development of novel materials and technologies to diminish viral availability, infectivity, and to enable enhanced preventive and therapeutic strategies, for the safety and well-being of humankind.

Rosa Vinicius, Ho Dean, Sabino-Silva Robinson, Siqueira Walter L, Silikas Nikolaos


COVID-19, Coating, Coronavirus, Diagnostic, Infection, Nanomaterial, Nanotechnology, Pandemic, Saliva, Vaccine

General General

Deep learning in the quest for compound nomination for fighting COVID-19.

In Current medicinal chemistry ; h5-index 49.0

The current COVID-19 pandemic gave rise to an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. This created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, in diagnostics or drug discovery and repurposing. More is expected to come in the near future from using such advanced machine learning techniques in combating this pandemic. This review is aimed to uncover just a small fraction of this large global endeavor by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found in confronting COVID-19 or alleviating its symptoms in the absence of vaccines or specific medication.

Mernea Maria, Martin Eliza C, Petrescu Andrei-José, Avram Speranta


SARS-CoV-2, deep learning, drug design, drug repurposing.\n, drug-target interactions, virtual screening

General General

COVID-19: Short-term forecast of ICU beds in times of crisis.

In PloS one ; h5-index 176.0

By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.

Goic Marcel, Bozanic-Leal Mirko S, Badal Magdalena, Basso Leonardo J


General General

Are college campuses superspreaders? A data-driven modeling study.

In Computer methods in biomechanics and biomedical engineering

The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.

Lu Hannah, Weintz Cortney, Pace Joseph, Indana Dhiraj, Linka Kevin, Kuhl Ellen


COVID-19, Coronavirus, SEIR model, epidemiology, machine learning

General General

An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network.

In Current medical imaging

BACKGROUND : Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population.

OBJECTIVE : This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs.

METHODS : A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset.

RESULTS AND CONCLUSION : For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient's images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Rao P Srinivasa, Bheemavarapu Pradeep, Kalyampudi P S Latha, Rao T V Madhusudhana


Coronavirus, VGG19, chest x-ray radiographs, convolutional neural network., covid-19, real time – polymerase chain reaction

General General

Pandemic Equation for Describing and Predicting COVID19 Evolution.

In Journal of healthcare informatics research

The purpose of this work is to describe the dynamics of the COVID-19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced. The methods used include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease and mitigation measures. The Pandemic Equation predicts how the quarantine removal and business opening lead to a spike in the pandemic curve. The effective vaccination reduces the new daily infections predicted by the Pandemic Equation. The pandemic curves in many localities have similar time dependencies but shifted in time. The Pandemic Equation parameters extracted from the well advanced pandemic curves can be used for predicting the pandemic evolution in the localities, where the pandemics is still in the initial stages. Using the multiple pandemic locations for the parameter extraction allows for the uncertainty quantification in predicting the pandemic evolution using the introduced Pandemic Equation. Compared with other pandemic models our approach allows for easier parameter extraction amenable to using Artificial Intelligence models.

Shur Michael


COVID19, Mitigation, Pandemic, Quarantine

General General

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms.

In Multimedia tools and applications

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.

Arpaci Ibrahim, Huang Shigao, Al-Emran Mostafa, Al-Kabi Mohammed N, Peng Minfei


COVID-19, Classification algorithms, Diagnosis, Machine learning, Novel coronavirus, Prediction

General General

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

In Neural computing & applications

COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Singh Rajeev Kumar, Pandey Rohan, Babu Rishie Nandhan


COVID-19, Chest X-rays, Deep learning, Ensemble learning, ExplainableAI, GANs

General General

The association of Coronavirus Disease-19 mortality and prior bacille Calmette-Guerin vaccination: a robust ecological analysis using unsupervised machine learning.

In Scientific reports ; h5-index 158.0

Population-level data have suggested that bacille Calmette-Guerin (BCG) vaccination may lessen the severity of Coronavirus Disease-19 (COVID-19) prompting clinical trials in this area. Some reports have demonstrated conflicting results. We performed a robust, ecologic analysis comparing COVID-19 related mortality (CRM) between strictly selected countries based on BCG vaccination program status utilizing publicly available databases and machine learning methods to define the association between active BCG vaccination programs and CRM. Validation was performed using linear regression and country-specific modeling. CRM was lower for the majority of countries with a BCG vaccination policy for at least the preceding 15 years (BCG15). CRM increased significantly for each increase in the percent population over age 65. A higher total population of a country and BCG15 were significantly associated with improved CRM. There was a consistent association between countries with a BCG vaccination for the preceding 15 years, but not other vaccination programs, and CRM. BCG vaccination programs continued to be associated with decreased CRM even for populations < 40 years old where CRM events are less frequent.

Brooks Nathan A, Puri Ankur, Garg Sanya, Nag Swapnika, Corbo Jacomo, Turabi Anas El, Kaka Noshir, Zemmel Rodney W, Hegarty Paul K, Kamat Ashish M


Ophthalmology Ophthalmology

Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.

In Journal of diabetes science and technology ; h5-index 38.0

BACKGROUND : Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting.

METHOD : Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR.

RESULTS : A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading.

CONCLUSIONS : The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

Malerbi Fernando Korn, Andrade Rafael Ernane, Morales Paulo Henrique, Stuchi José Augusto, Lencione Diego, de Paulo Jean Vitor, Carvalho Mayana Pereira, Nunes Fabrícia Silva, Rocha Roseanne Montargil, Ferraz Daniel A, Belfort Rubens


Covid-19, artificial intelligence, diabetic retinopathy, mobile healthcare, point-of-care, screening, telemedicine

Public Health Public Health

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.

In Interdisciplinary sciences, computational life sciences

The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions of new types of corona virus is vital in clinical studies, drug therapy, identification of preclinical compounds and protein functions. Protein-protein interactions are important to examine protein functions and pathways involved in various biological processes and to determine the cause and progression of diseases. Various high-throughput experimental methods have been used to identify protein-protein interactions in organisms, yet, there is still a huge gap in specifying all possible protein interactions in an organism. In addition, since the experimental methods used include cloning, labeling, affinity purification mass spectrometry, the processes take a long time. Determining these interactions with artificial intelligence-based methods rather than experimental approaches may help to identify protein functions faster. Thus, protein-protein interaction prediction using deep-learning algorithms has been employed in conjunction with experimental method to explore new protein interactions. However, to predict protein interactions with artificial intelligence techniques, protein sequences need to be mapped. There are various types and numbers of protein-mapping methods in the literature. In this study, we wanted to contribute to the literature by proposing a novel protein-mapping method based on the AVL tree. The proposed method was inspired by the fast search performance on the dictionary structure of AVL tree and was used to verify the protein interactions between SARS-COV-2 virus and human. First, protein sequences were mapped by both the proposed method and various protein-mapping methods. Then, the mapped protein sequences were normalized and classified by bidirectional recurrent neural networks. The performance of the proposed method was evaluated with accuracy, f1-score, precision, recall, and AUC scores. Our results indicated that our mapping method predicts the protein interactions between SARS-COV-2 virus proteins and human proteins at an accuracy of 97.76%, precision of 97.60%, recall of 98.33%, f1-score of 79.42%, and with AUC 89% in average.

Alakus Talha Burak, Turkoglu Ibrahim


AVL tree, COVID-19, Deep learning, Protein mapping, SARS-COV-2

General General

Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images.

In Signal, image and video processing

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

Kc Kamal, Yin Zhendong, Wu Mingyang, Wu Zhilu


COVID-19, Chest X-ray, Deep convolution neural network, SARS, Transfer learning

Radiology Radiology

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).

In Scientific reports ; h5-index 158.0

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

Pan Feng, Li Lin, Liu Bo, Ye Tianhe, Li Lingli, Liu Dehan, Ding Zezhen, Chen Guangfeng, Liang Bo, Yang Lian, Zheng Chuansheng


General General

Using image-based haplotype alignments to map global adaptation of SARS-CoV-2

bioRxiv Preprint

Quantifying evolutionary change among viral genomes is an important clinical device to track critical adaptations geographically and temporally. We built image-based haplotype-guided evolutionary inference (ImHapE) to quantify adaptations in expanding populations of non-recombining SARS-CoV-2 genomes. By combining classic population genetic summaries with image-based deep learning methods, we show that different rates of positive selection are driving evolutionary fitness and dispersal of SARS-CoV-2 globally. A 1.35-fold increase in evolutionary fitness is observed within the UK, associated with expansion of both the B.1.177 and B.1.1.7 SARS-CoV-2 lineages.

Ouellette, T. W.; Shaw, J.; Awadalla, P.


General General

Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19.

In Cognitive computation

The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.

Ibrahim Abdullahi Umar, Ozsoz Mehmet, Serte Sertan, Al-Turjman Fadi, Yakoi Polycarp Shizawaliyi


AlexNet, Bacterial pneumonia, COVID-19, Chest X-rays images (CXR), Non-COVID-19 viral pneumonia

General General

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset.

In Cognitive computation

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90-10%, 80-20%, 70-30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

Khalifa Nour Eldeen M, Smarandache Florentin, Manogaran Gunasekaran, Loey Mohamed


CNN, COVID-19, Coronavirus, Deep transfer learning, Neutrosophic, SARS-CoV-2

General General

Sociological modeling of smart city with the implementation of UN sustainable development goals.

In Sustainability science

** : The COVID-19 pandemic before mass vaccination can be restrained only by the limitation of contacts between people, which makes the digital economy a key condition for survival. More than half of the world's population lives in urban areas, and many cities have already transformed into "smart" digital/virtual hubs. Digital services ensure city life safe without an economy lockout and unemployment. Urban society strives to be safe, sustainable, well-being, and healthy. We set the task to construct a hybrid sociological and technological concept of a smart city with matched solutions, complementary to each other. Our modeling with the elaborated digital architectures and with the bionic solution for ensuring sufficient data governance showed that a smart city in comparison with the traditional city is tightly interconnected inside like a social "organism". Society has entered a decisive decade during which the world will change by moving closer towards SDGs targets 2030 as well as by the transformation of cities and their digital infrastructures. It is important to recognize the large vector of sociological transformation as smart cities are just a transition phase to human-centered personal space or smart home. The "atomization" of the world urban population raises the gap problem in achieving SDGs because of different approaches to constructing digital architectures for smart cities or smart homes in countries. The strategy of creating smart cities should bring each citizen closer to SDGs at the individual level, laying in the personal space the principles of sustainable development and wellness of personality.

Supplementary Information : The online version contains supplementary material available at 10.1007/s11625-020-00889-5.

Kolesnichenko Olga, Mazelis Lev, Sotnik Alexander, Yakovleva Dariya, Amelkin Sergey, Grigorevsky Ivan, Kolesnichenko Yuriy


API-sociology, Community wellness, Logical artificial intelligence, Smart and healthy city, Sociology of smart city, Sustainable development goals

Radiology Radiology

Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML.

In Soft computing

The COVID-19 pandemic continues to have a destructive effect on the health and well-being of the global population. A vital step in the battle against it is the successful screening of infected patients, together with one of the effective screening methods being radiology examination using chest radiography. Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. The study's primary motivation is to be able to estimate with a certain level of accuracy the number of deaths due to COVID-19, managing to model the progression of the pandemic. Predicting the number of possible deaths from COVID-19 can provide governments and decision-makers with indicators for purchasing respirators and pandemic prevention policies. Thus, this work presents itself as an essential contribution to combating the pandemic. Kalman Filter is a widely used method for tracking and navigation and filtering and time series. Designing and tuning machine learning methods are a labor- and time-intensive task that requires extensive experience. The field of automated machine learning Auto Machine Learning relies on automating this task. Auto Machine Learning tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. This paper presents an objective method of forecasting the COVID-19 outbreak using Kalman Filter and Auto Machine Learning. We use a COVID-19 dataset of Ceará, one of the 27 federative units in Brazil. Ceará has more than 235,222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The TPOT automobile model showed the best result with a 0.99 of R 2 score.

Han Tao, Gois Francisco Nauber Bernardo, Oliveira Ramsés, Prates Luan Rocha, Porto Magda Moura de Almeida


AutoML, COVID-19, Forecast, Kalman Filter

Public Health Public Health

Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method.

In Neural computing & applications

** : To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity.

Supplementary Information : The online version contains supplementary material available at(10.1007/s00521-020-05592-1).

Li Simin, Lin Yulan, Zhu Tong, Fan Mengjie, Xu Shicheng, Qiu Weihao, Chen Can, Li Linfeng, Wang Yao, Yan Jun, Wong Justin, Naing Lin, Xu Shabei


COVID-19, China, Machine learning, Mortality, Prediction

General General

Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning.

In The Journal of supercomputing

In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription-polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2-4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency-inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients.

Ramanathan Shalini, Ramasundaram Mohan


COVID-19, Classification, Feature extraction, Machine learning, RT-PCR test, TF-IDF, Text data mining

General General

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.

In Annals of operations research

World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

Khalilpourazari Soheyl, Hashemi Doulabi Hossein


COVID-19 pandemic, Machine learning, Reinforcement learning, SARS-Cov-2, SIDARTHE

General General

Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared.

In Journal of nondestructive evaluation

Convolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

Müller David, Ehlen Andreas, Valeske Bernd


Artificial intelligence, Health monitoring, Intelligent sensors, Machine learning, Thermography

General General

Big data augmentated business trend identification: the case of mobile commerce.

In Scientometrics

Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

Saritas Ozcan, Bakhtin Pavel, Kuzminov Ilya, Khabirova Elena


COVID-19, Global trends, Horizon scanning, M-commerce, Machine learning, Natural language processing, Tech mining

General General

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.

In International journal of nursing studies advances

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Keim-Malpass Jessica, Moorman Liza P


Acuity assessment, COVID-19, Clinical deterioration, Continuous predictive analytics monitoring, Nursing, Precision surveillance

General General

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways-firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Shastri Sourabh, Singh Kuljeet, Kumar Sachin, Kour Paramjit, Mansotra Vibhakar


Artificial intelligence, Covid-19, Deep learning, LSTM, Nested ensemble

General General

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images.

In Frontiers in medicine

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

Gunraj Hayden, Wang Linda, Wong Alexander


COVID-19, SARS-CoV-2, computed tomography, deep learning, image classification, pneumonia

General General

Predicting the European stock market during COVID-19: A machine learning approach.

In MethodsX

This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.

Khattak Mudeer Ahmed, Ali Mohsin, Rizvi Syed Aun R


Coronavirus, Europe, Least Absolute Shrinkage and Selection Operator (LASSO), Stock markets

General General

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2.

In Sustainable cities and society

Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have been created using several algorithms and techniques. The proposed approach in this paper uses deep learning, TensorFlow, Keras, and OpenCV to detect face masks. This model can be used for safety purposes since it is very resource efficient to deploy. The SSDMNV2 approach uses Single Shot Multibox Detector as a face detector and MobilenetV2 architecture as a framework for the classifier, which is very lightweight and can even be used in embedded devices (like NVIDIA Jetson Nano, Raspberry pi) to perform real-time mask detection. The technique deployed in this paper gives us an accuracy score of 0.9264 and an F1 score of 0.93. The dataset provided in this paper, was collected from various sources, can be used by other researchers for further advanced models such as those of face recognition, facial landmarks, and facial part detection process.

Nagrath Preeti, Jain Rachna, Madan Agam, Arora Rohan, Kataria Piyush, Hemanth Jude


Bottleneck, Convolutional Neural Network, Data augmentation, Fine tuning, MobileNetV2

General General

E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network.

In Journal of ambient intelligence and humanized computing

The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.

Murugan R, Goel Tripti


Automatic diagnosis, COVID-19, Convolutional neural network, Coronavirus, Deep learning, Extreme learning machine, ResNet

General General

COV19-CNNet and COV19-ResNet: Diagnostic Inference Engines for Early Detection of COVID-19.

In Cognitive computation

Chest CT is used in the COVID-19 diagnosis process as a significant complement to the reverse transcription polymerase chain reaction (RT-PCR) technique. However, it has several drawbacks, including long disinfection and ventilation times, excessive radiation effects, and high costs. While X-ray radiography is more useful for detecting COVID-19, it is insensitive to the early stages of the disease. We have developed inference engines that will turn X-ray machines into powerful diagnostic tools by using deep learning technology to detect COVID-19. We named these engines COV19-CNNet and COV19-ResNet. The former is based on convolutional neural network architecture; the latter is on residual neural network (ResNet) architecture. This research is a retrospective study. The database consists of 210 COVID-19, 350 viral pneumonia, and 350 normal (healthy) chest X-ray (CXR) images that were created using two different data sources. This study was focused on the problem of multi-class classification (COVID-19, viral pneumonia, and normal), which is a rather difficult task for the diagnosis of COVID-19. The classification accuracy levels for COV19-ResNet and COV19-CNNet were 97.61% and 94.28%, respectively. The inference engines were developed from scratch using new and special deep neural networks without pre-trained models, unlike other studies in the field. These powerful diagnostic engines allow for the early detection of COVID-19 as well as distinguish it from viral pneumonia with similar radiological appearances. Thus, they can help in fast recovery at the early stages, prevent the COVID-19 outbreak from spreading, and contribute to reducing pressure on health-care systems worldwide.

Keles Ayturk, Keles Mustafa Berk, Keles Ali


CXR radiographs, Convolutional neural network, Novel coronavirus, Pneumonia, Residual network, SARS-CoV-2

Radiology Radiology

Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm.

In Acta diabetologica ; h5-index 40.0

AIMS : Increasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome.

METHODS : A total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions.

RESULTS : Patients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (β = 0.401, p < 0.001), PGV (β = 0.364, p = 0.001) and PCV (β = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes.

CONCLUSIONS : Increased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.

Lu Xiaoting, Cui Zhenhai, Pan Feng, Li Lingli, Li Lin, Liang Bo, Yang Lian, Zheng Chuansheng


AI, Blood glucose, COVID-19, CT, DM

General General

How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning.

In Journal of religion and health

Nowadays, artificial intelligence (AI) and machine learning (ML) are playing a tremendous role in all aspects of human life and they have the remarkable potential to solve many problems that classic sciences are unable to solve appropriately. Neuroscience and especially psychiatry is one of the most important fields that can use the potential of AI and ML. This study aims to develop an ML-based model to detect the relationship between resiliency and hope with the stress of COVID-19 by mediating the role of spiritual well-being. An online survey is conducted to assess the psychological responses of Iranian people during the Covid-19 outbreak in the period between March 15 and May 20, 2020, in Iran. The Iranian public was encouraged to take part in an online survey promoted by Internet ads, e-mails, forums, social networks, and short message service (SMS) programs. As a whole, 755 people participated in this study. Sociodemographic characteristics of the participants, The Resilience Scale, The Adult Hope Scale, Paloutzian & Ellison's Spiritual Wellbeing Scale, and Stress of Covid-19 Scale were used to gather data. The findings showed that spiritual well-being itself cannot predict stress of Covid-19 alone, and in fact, someone who has high spiritual well-being does not necessarily have a small amount of stress, and this variable, along with hope and resiliency, can be a good predictor of stress. Our extensive research indicated that traditional analytical and statistical methods are unable to correctly predict related Covid-19 outbreak factors, especially stress when benchmarked with our proposed ML-based model which can accurately capture the nonlinear relationships between the collected data variables.

Nooripour Roghieh, Hosseinian Simin, Hussain Abir Jaafar, Annabestani Mohsen, Maadal Ameer, Radwin Laurel E, Hassani-Abharian Peyman, Pirkashani Nikzad Ghanbari, Khoshkonesh Abolghasem


Covid-19, Hope, Machine learning, Resiliency, Spiritual well-being, Stress

General General

How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study.

In medRxiv : the preprint server for health sciences

Background : With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020.

Methods : We used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance.

Findings : Following high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1·0 million (95% prediction interval 0·5 - 1·7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with a >99% chance of exceeding Covid-19-specific capacity (9,667 beds). If holiday contacts can be controlled, MCMA can reopen in-person schools provided social distancing is increased with 0·5 million (0·2 - 1·0) additional cases and hospitalizations peaking at 14,900 (5,600 - 32,000) on January 23, 2021 (77% chance of exceedance).

Interpretation : MCMA must substantially increase Covid-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

Funding : Society for Medical Decision Making, Gordon and Betty Moore Foundation, and Wadhwani Institute for Artificial Intelligence Foundation.

Research in context : Evidence before this study: As of mid-December 2020, Mexico has the twelfth highest incidence of confirmed cases of Covid-19 worldwide and its epidemic is currently growing. Mexico's case fatality ratio (CFR) - 9·1% - is the second highest in the world. With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the highest number and incidence rate of Covid-19 confirmed cases in Mexico and a CFR of 8·1%. MCMA is nearing its current hospital capacity even as it faces the prospect of increased social contacts during the 2020 end-of-year holidays. There is limited Mexico-specific evidence available on epidemic, such as parameters governing time-dependent mortality, hospitalization and transmission. Literature searches required supplementation through primary data analysis and model calibration to support the first realistic model-based Covid-19 policy evaluation for Mexico, which makes this analysis relevant and timely.Added value of this study: Study strengths include the use of detailed primary data provided by MCMA; the Bayesian model calibration to enable evaluation of projections and their uncertainty; and consideration of both epidemic and health system outcomes. The model projects that failure to limit social contacts during the end-of-year holidays will substantially accelerate MCMA's epidemic (1·0 million (95% prediction interval 0·5 - 1·7) additional cases by early March 2021). Hospitalization demand could reach 35,000 (14,700 - 67,500), with a >99% chance of exceeding current capacity (9,667 beds). Controlling social contacts during the holidays could enable MCMA to reopen in-person schooling without greatly exacerbating the epidemic provided social distancing in both schools and the community were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion.Implications of all the available evidence: MCMA officials should prioritize rapid hospital capacity expansion. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

Alarid-Escudero Fernando, Gracia Valeria, Luviano Andrea, Peralta Yadira, Reitsma Marissa B, Claypool Anneke L, Salomon Joshua A, Studdert David M, Andrews Jason R, Goldhaber-Fiebert Jeremy D


General General

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity.

In bioRxiv : the preprint server for biology

** : T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN model as the best prediction model based on its adaptivity for small and large datasets, and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepHLApan and IEDB, DeepImmuno-CNN further correctly predicts which residues are most important for T cell antigen recognition. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physiochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.

Data Availability : DeepImmuno Python3 code is available at . The DeepImmuno web portal is available from . The data in this article is available in GitHub and supplementary materials.

Li Guangyuan, Iyer Balaji, Prasath V B Surya, Ni Yizhao, Salomonis Nathan


General General

Analyzing the vast coronavirus literature with CoronaCentral.

In bioRxiv : the preprint server for biology

The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as "Long Covid" and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention. This resource, available at , is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.

Lever Jake, Altman Russ B


General General

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

In bioRxiv : the preprint server for biology

Background : Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.

Methods : We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20 th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states.

Results : GNUVID is a scalable tool for viral genotype classification (available at ) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed.

Conclusions : Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.

Moustafa Ahmed M, Planet Paul J


General General

Machine learning-based prediction of COVID-19 diagnosis based on symptoms.

In NPJ digital medicine

Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Zoabi Yazeed, Deri-Rozov Shira, Shomron Noam


Public Health Public Health

A Sentiment Analysis Approach to Predict an Individual's Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.

In International journal of environmental research and public health ; h5-index 73.0

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual's awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency-Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

Aljameel Sumayh S, Alabbad Dina A, Alzahrani Norah A, Alqarni Shouq M, Alamoudi Fatimah A, Babili Lana M, Aljaafary Somiah K, Alshamrani Fatima M


Arabic sentiment analysis, K-nearest neighbor, N-gram, Twitter, machine learning, natural language processing, naïve bayes, support vector machine

Radiology Radiology

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

In Diagnostics (Basel, Switzerland)

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Guiot Julien, Vaidyanathan Akshayaa, Deprez Louis, Zerka Fadila, Danthine Denis, Frix Anne-Noëlle, Thys Marie, Henket Monique, Canivet Gregory, Mathieu Stephane, Eftaxia Evanthia, Lambin Philippe, Tsoutzidis Nathan, Miraglio Benjamin, Walsh Sean, Moutschen Michel, Louis Renaud, Meunier Paul, Vos Wim, Leijenaar Ralph T H, Lovinfosse Pierre


COVID-19, artificial intelligence, computed tomography, machine learning, radiomics

Public Health Public Health

Artificial Intelligence Model of Drive-Through Vaccination Simulation.

In International journal of environmental research and public health ; h5-index 73.0

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

Asgary Ali, Valtchev Svetozar Zarko, Chen Michael, Najafabadi Mahdi M, Wu Jianhong


COVID-19 pandemic, artificial intelligence, discrete event simulation, drive-through, mass vaccination

Radiology Radiology

Factors associated with worsening oxygenation in patient with non-severe COVID-19 pneumonia.

In Tuberculosis and respiratory diseases

Background : This study aimed to determine parameters for worsening oxygenation in non-severe COVID-19 pneumonia.

Methods : This retrospective cohort study included confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as those with SpO2 ≤ 94%, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening group who were without any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest CT were calculated using artificial intelligence (AI) and measured visually by a radiologist.

Results : We included 136 patients with 32 (23.5%) in the worsening oxygenation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. A subset of patients were studied to eliminate potential bias, that ferritin ≥ 280 μg/L (p=0.029), LDH ≥ 240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52).

Conclusion : Our study presents initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation at admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

Hahm Cho Rom, Lee Young Kyung, Oh Dong Hyun, Ahn Mi Young, Choi Jae-Phil, Kang Na Ree, Oh Jungkyun, Choi Hanzo, Kim Suhyun


COVID-19, Computed tomography, Oxygenation, Pneumonia, artificial intelligence

Radiology Radiology

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Current ML studies focusing on coronavirus disease 2019 (COVID-19) are limited to single hospital data which limits model generalizability.

OBJECTIVE : Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients.

METHODS : Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator.

RESULTS : LASSO-federated outperformed LASSO-local at three hospitals, and MLP-federated performed better than MLP-local at all five hospitals as measured by area under the receiver-operating characteristic (AUC-ROC). LASSO-pooled outperformed LASSO-federated at all hospitals, and MLP-federated outperformed MLP-pooled at two hospitals.

CONCLUSIONS : Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.


Vaid Akhil, Jaladanki Suraj K, Xu Jie, Teng Shelly, Kumar Arvind, Lee Samuel, Somani Sulaiman, Paranjpe Ishan, De Freitas Jessica K, Wanyan Tingyi, Johnson Kipp W, Bicak Mesude, Klang Eyal, Kwon Young Joon, Costa Anthony, Zhao Shan, Miotto Riccardo, Charney Alexander W, Böttinger Erwin, Fayad Zahi A, Nadkarni Girish N, Wang Fei, Glicksberg Benjamin S


Radiology Radiology

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD).

In Radiology ; h5-index 91.0

The coronavirus disease 2019 (COVID-19) pandemic is a global healthcare emergency. Although reverse transcriptase polymerase chain reaction (RT-PCR) is the reference standard method to identify patients with COVID-19 infection, chest radiographs and CT chest play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology (STR) collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations were performed by thoracic radiology subspecialists for all COVID positive thoracic CTs. The labeling schema was coordinated with other international consensus panels and COVID data annotation efforts, European Society of Medical Imaging Informatics (EUSOMII), the American College of Radiology (ACR) and the American Association of Physicists in Medicine (AAPM). Study level COVID classification labels for chest radiographs were annotated by three radiologists with majority vote adjudication by board certified radiologists. RICORD consists of 240 thoracic CT scans and 1,000 chest radiographs contributed from four international sites. We anticipate that the RICORD database will ideally lead to prediction models that can demonstrate sustained performance across populations and healthcare systems. See also the editorial by Bai and Thomasian.

Tsai Emily B, Simpson Scott, Lungren Matthew, Hershman Michelle, Roshkovan Leonid, Colak Errol, Erickson Bradley J, Shih George, Stein Anouk, Kalpathy-Cramer Jaysheree, Shen Jody, Hafez Mona, John Susan, Rajiah Prabhakar, Pogatchnik Brian P, Mongan John, Altinmakas Emre, Ranschaert Erik R, Kitamura Felipe C, Topff Laurens, Moy Linda, Kanne Jeffrey P, Wu Carol C


Radiology Radiology

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.

In Journal of the American College of Emergency Physicians open

Objective : The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.

Methods : During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking.

Results : Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking.

Conclusions : To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.

Carlile Morgan, Hurt Brian, Hsiao Albert, Hogarth Michael, Longhurst Christopher A, Dameff Christian


Algorithms, COVID‐19, artificial intelligence, computers and society, deep learning, emergency medicine, informatics, machine learning, radiology

Radiology Radiology

FCOD: Fast COVID-19 Detector based on deep learning techniques.

In Informatics in medicine unlocked

The sudden COVID-19 pandemic has caused a serious global concern due to infections and mortality rates. It is a hazardous disease that has recently become the biggest crisis in the modern era. Due to the limitation of test kits and the need for screening and rapid diagnosis of patients, it is essential to perform a self-operating detection model as a fast recognition system to detect COVID-19 infection and prevent the spread among the people. In this paper, we propose a novel technique called Fast COVID-19 Detector (FCOD) to have a fast detection of COVID-19 using X-ray images. The FCOD is a deep learning model based on the Inception architecture that uses 17 depthwise separable convolution layers to detect COVID-19. Depthwise separable convolution layers decrease the computation costs, time, and they can have a reducing role in the number of parameters compared to the standard convolution layers. To evaluate FCOD, we used covid-chestxray-dataset, which contains 940 publicly available typical chest X-ray images. Our results show that FCOD can provide accuracy, F1-score, and AUC of 96%, 96%, and 0.95%, respectively in classifying COVID-19 during 0.014 s for each case. The proposed model can be employed as a supportive decision-making system to assist radiologists in clinics and hospitals to screen patients immediately.

Panahi Amir Hossein, Rafiei Alireza, Rezaee Alireza


COVID-19 detection, Chest X-ray images, Deep learning, Image processing, Medical applications, Radiology images

Dermatology Dermatology

Clinical Characteristics and Neonatal Outcomes of Pregnant Patients With COVID-19: A Systematic Review.

In Frontiers in medicine

Background and Objective: Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and neonatal outcomes of pregnant women with COVID-19. Methods: We conducted a systematic article search of PubMed, EMBASE, Scopus, Google Scholar, and Web of Science for studies that discussed pregnant patients with confirmed COVID-19 between January 1, 2020, and April 20, 2020, with no restriction on language. Articles were independently evaluated by two expert authors. We included all retrospective studies that reported the clinical features and outcomes of pregnant patients with COVID-19. Results: Forty-seven articles were assessed for eligibility; 13 articles met the inclusion criteria for the systematic review. Data is reported for 235 pregnant women with COVID-19. The age range of patients was 25-40 years, and the gestational age ranged from 8 to 40 weeks plus 6 days. Clinical characteristics were fever [138/235 (58.72%)], cough [111/235 (47.23%)], and sore throat [21/235 (8.93%)]. One hundred fifty six out of 235 (66.38%) pregnant women had cesarean section, and 79 (33.62%) had a vaginal delivery. All the patients showed lung abnormalities in CT scan images, and none of the patients died. Neutrophil cell count, C-reactive protein (CRP) concentration, ALT, and AST were increased but lymphocyte count and albumin levels were decreased. Amniotic fluid, neonatal throat swab, and breastmilk samples were taken to test for SARS-CoV-2 but all found negativ results. Recent published evidence showed the possibility of vertical transmission up to 30%, and neonatal death up to 2.5%. Pre-eclampsia, fetal distress, PROM, pre-mature delivery were the major complications of pregnant women with COVID-19. Conclusions: Our study findings show that the clinical, laboratory and radiological characteristics of pregnant women with COVID-19 were similar to those of the general populations. The possibility of vertical transmission cannot be ignored but C-section should not be routinely recommended anymore according to latest evidences and, in any case, decisions should be taken after proper discussion with the family. Future studies are needed to confirm or refute these findings with a larger number of sample sizes and a long-term follow-up period.

Islam Md Mohaimenul, Poly Tahmina Nasrin, Walther Bruno Andreas, Yang Hsuan Chia, Wang Cheng-Wei, Hsieh Wen-Shyang, Atique Suleman, Salmani Hosna, Alsinglawi Belal, Lin Ming Ching, Jian Wen Shan, Jack Li Yu-Chuan


COVID-19, CT-scan, SARS–CoV-2, coronavirus, pregnant women

General General

Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic-A South American Perspective.

In Frontiers in public health

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic.

Chatterjee Parag, Tesis Andreína, Cymberknop Leandro J, Armentano Ricardo L


COVID-19, artificial intelligence, healthcare, internet of things, machine learning, pandemic, ubiquitous, virtual healthcare

Cardiology Cardiology

ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study.

In Royal Society open science

Angiotensin-converting enzyme 2 (ACE2) and serine protease TMPRSS2 have been implicated in cell entry for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). The expression of ACE2 and TMPRSS2 in the lung epithelium might have implications for the risk of SARS-CoV-2 infection and severity of COVID-19. We use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to investigate whether these exposures affect lung ACE2 and TMPRSS2 gene expression and circulating ACE2 levels. We observed no consistent evidence of an association of genetically predicted serum ACE levels with any of our outcomes. There was weak evidence for an association of genetically predicted serum ACE levels with ACE2 gene expression in the Lung eQTL Consortium (p = 0.014), but this finding did not replicate. There was evidence of a positive association of genetic liability to type 2 diabetes mellitus with lung ACE2 gene expression in the Gene-Tissue Expression (GTEx) study (p = 4 × 10-4) and with circulating plasma ACE2 levels in the INTERVAL study (p = 0.03), but not with lung ACE2 expression in the Lung eQTL Consortium study (p = 0.68). There were no associations of genetically proxied liability to the other cardiometabolic traits with any outcome. This study does not provide consistent evidence to support an effect of serum ACE levels (as a proxy for ACE inhibitors) or cardiometabolic risk factors on lung ACE2 and TMPRSS2 expression or plasma ACE2 levels.

Gill Dipender, Arvanitis Marios, Carter Paul, Hernández Cordero Ana I, Jo Brian, Karhunen Ville, Larsson Susanna C, Li Xuan, Lockhart Sam M, Mason Amy, Pashos Evanthia, Saha Ashis, Tan Vanessa Y, Zuber Verena, Bossé Yohan, Fahle Sarah, Hao Ke, Jiang Tao, Joubert Philippe, Lunt Alan C, Ouwehand Willem Hendrik, Roberts David J, Timens Wim, van den Berge Maarten, Watkins Nicholas A, Battle Alexis, Butterworth Adam S, Danesh John, Di Angelantonio Emanuele, Engelhardt Barbara E, Peters James E, Sin Don D, Burgess Stephen


COVID-19, Mendelian randomization, angiotensin-converting enzyme inhibitors, genetic epidemiology

General General

Deep Phenotyping of Headache in Hospitalized COVID-19 Patients via Principal Component Analysis.

In Frontiers in neurology

Objectives: Headache is a common symptom in systemic infections, and one of the symptoms of the novel coronavirus disease 2019 (COVID-19). The objective of this study was to characterize the phenotype of COVID-19 headache via machine learning. Methods: We performed a cross-sectional study nested in a retrospective cohort. Hospitalized patients with COVID-19 confirmed diagnosis who described headache were included in the study. Generalized Linear Models and Principal Component Analysis were employed to detect associations between intensity and self-reported disability caused by headache, quality and topography of headache, migraine features, COVID-19 symptoms, and results from laboratory tests. Results: One hundred and six patients were included in the study, with a mean age of 56.6 ± 11.2, including 68 (64.2%) females. Higher intensity and/or disability caused by headache were associated with female sex, fever, abnormal platelet count and leukocytosis, as well as migraine symptoms such as aggravation by physical activity, pulsating pain, and simultaneous photophobia and phonophobia. Pain in the frontal area (83.0% of the sample), pulsating quality, higher intensity of pain, and presence of nausea were related to lymphopenia. Pressing pain and lack of aggravation by routine physical activity were linked to low C-reactive protein and procalcitonin levels. Conclusion: Intensity and disability caused by headache attributed to COVID-19 are associated with the disease state and symptoms. Two distinct headache phenotypes were observed in relation with COVID-19 status. One phenotype seems to associate migraine symptoms with hematologic and inflammatory biomarkers of severe COVID-19; while another phenotype would link tension-type headache symptoms to milder COVID-19.

Planchuelo-Gómez Álvaro, Trigo Javier, de Luis-García Rodrigo, Guerrero Ángel L, Porta-Etessam Jesús, García-Azorín David


COVID-19, headache disorders, machine learning, migraine, tension-type headache

General General

Multi-criterion Intelligent Decision Support system for COVID-19.

In Applied soft computing

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is "if the disease can travel faster information has to travel even faster". The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.

Aggarwal Lakshita, Goswami Puneet, Sachdeva Shelly


Covid-19, Epidemiology, Learning method, Machine learning, Multi-criterion Intelligent Decision Support system

General General

Risk factors related to the severity of COVID-19 in Wuhan.

In International journal of medical sciences

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.

Zhao Chen, Bai Yan, Wang Cencen, Zhong Yanyan, Lu Na, Tian Li, Cai Fucheng, Jin Runming


COVID-19, machine learning, moderate cases, prognostic prediction, risk factors, severe prognosis, severity

General General

ResGNet-C: A graph convolutional neural network for detection of COVID-19.

In Neurocomputing

The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.

Yu Xiang, Lu Siyuan, Guo Lili, Wang Shui-Hua, Zhang Yu-Dong


COVID-19, Deep learning, Graph neural network, Pneumonia, ResGNet-C

General General

A National US Survey of Pediatric Emergency Department Coronavirus Pandemic Preparedness.

In Pediatric emergency care

OBJECTIVE : We aim to describe the current coronavirus disease 2019 (COVID-19) preparedness efforts among a diverse set of pediatric emergency departments (PEDs) within the United States.

METHODS : We conducted a prospective multicenter survey of PED medical director(s) from selected children's hospitals recruited through a long established national research network. The questionnaire was developed by physicians with expertise in pediatric emergency medicine, disaster readiness, human factors, and survey development. Thirty-five children's hospitals were identified for recruitment through an established national research network.

RESULTS : We report on survey responses from 25 (71%) of 35 PEDs, of which 64% were located within academic children's hospitals. All PEDs witnessed decreases in non-COVID-19 patients, 60% had COVID-19-dedicated units, and 32% changed their unit pediatric patient age to include adult patients. All PEDs implemented changes to their staffing model, with the most common change impacting their physician staffing (80%) and triaging model (76%). All PEDs conducted training for appropriate donning and doffing of personal protective equipment (PPE), and 62% reported shortages in PPE. The majority implemented changes in the airway management protocols (84%) and cardiac arrest management in COVID patients (76%). The most common training modalities were video/teleconference (84%) and simulation-based training (72%). The most common learning objectives were team dynamics (60%), and PPE and individual procedural skills (56%).

CONCLUSIONS : This national survey provides insight into PED preparedness efforts, training innovations, and practice changes implemented during the start of COVID-19 pandemic. Pediatric emergency departments implemented broad strategies including modifications to staffing, workflow, and clinical practice while using video/teleconference and simulation as preferred training modalities. Further research is needed to advance the level of preparedness and support deep learning about which preparedness actions were effective for future pandemics.

Auerbach Marc A, Abulebda Kamal, Bona Anna Mary, Falvo Lauren, Hughes Patrick G, Wagner Michael, Barach Paul R, Ahmed Rami A


General General

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.

In Journal of visualized experiments : JoVE

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.

Cattabriga Arrigo, Cocozza Maria Adriana, Vara Giulio, Coppola Francesca, Golfieri Rita


General General

Immune Computation and COVID-19 Mortality: A Rationale for IVIg.

In Critical reviews in immunology

COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).

Cohen Irun R, Efroni Sol, Atlan Henri


General General

Ivermectin as a potential drug for treatment of COVID-19: an in-sync review with clinical and computational attributes.

In Pharmacological reports : PR

INTRODUCTION : COVID-19 cases are on surge; however, there is no efficient treatment or vaccine that can be used for its management. Numerous clinical trials are being reviewed for use of different drugs, biologics, and vaccines in COVID-19. A much empirical approach will be to repurpose existing drugs for which pharmacokinetic and safety data are available, because this will facilitate the process of drug development. The article discusses the evidence available for the use of Ivermectin, an anti-parasitic drug with antiviral properties, in COVID-19.

METHODS : A rational review of the drugs was carried out utilizing their clinically significant attributes. A more thorough understanding was met by virtual embodiment of the drug structure and realizable viral targets using artificial intelligence (AI)-based and molecular dynamics (MD)-simulation-based study.

CONCLUSION : Certain studies have highlighted the significance of ivermectin in COVID-19; however, it requires evidences from more Randomised Controlled Trials (RCTs) and dose- response studies to support its use. In silico-based analysis of ivermectin's molecular interaction specificity using AI and classical mechanics simulation-based methods indicates positive interaction of ivermectin with viral protein targets, which is leading for SARS-CoV 2 N-protein NTD (nucleocapsid protein N-terminal domain).

Kaur Harpinder, Shekhar Nishant, Sharma Saurabh, Sarma Phulen, Prakash Ajay, Medhi Bikash


COVID-19, Ivermectin, SARS-CoV-2, Treatment

Radiology Radiology

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

In Reviews in cardiovascular medicine

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Suri Jasjit S, Puvvula Anudeep, Majhail Misha, Biswas Mainak, Jamthikar Ankush D, Saba Luca, Faa Gavino, Singh Inder M, Oberleitner Ronald, Turk Monika, Srivastava Saurabh, Chadha Paramjit S, Suri Harman S, Johri Amer M, Nambi Vijay, Sanches J Miguel, Khanna Narendra N, Viskovic Klaudija, Mavrogeni Sophie, Laird John R, Bit Arindam, Pareek Gyan, Miner Martin, Balestrieri Antonella, Sfikakis Petros P, Tsoulfas George, Protogerou Athanasios, Misra Durga Prasanna, Agarwal Vikas, Kitas George D, Kolluri Raghu, Teji Jagjit, Porcu Michele, Al-Maini Mustafa, Agbakoba Ann, Sockalingam Meyypan, Sexena Ajit, Nicolaides Andrew, Sharma Aditya, Rathore Vijay, Viswanathan Vijay, Naidu Subbaram, Bhatt Deepak L


COVID-19, artificial intelligence, cardiovascular, myocarditis, non-invasive monitoring, risk assessment

General General

Interpretable detection of novel human viruses from genome sequencing data

bioRxiv Preprint

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

Bartoszewicz, J. M.; Seidel, A.; Renard, B. Y.


General General

National preparedness survey of pediatric intensive care units with simulation centers during the coronavirus pandemic.

In World journal of critical care medicine ; h5-index 22.0

BACKGROUND : The coronavirus disease pandemic caught many pediatric hospitals unprepared and has forced pediatric healthcare systems to scramble as they examine and plan for the optimal allocation of medical resources for the highest priority patients. There is limited data describing pediatric intensive care unit (PICU) preparedness and their health worker protections.

AIM : To describe the current coronavirus disease 2019 (COVID-19) preparedness efforts among a set of PICUs within a simulation-based network nationwide.

METHODS : A cross-sectional multi-center national survey of PICU medical director(s) from children's hospitals across the United States. The questionnaire was developed and reviewed by physicians with expertise in pediatric critical care, disaster readiness, human factors, and survey development. Thirty-five children's hospitals were identified for recruitment through a long-established national research network. The questions focused on six themes: (1) PICU and medical director demographics; (2) Pediatric patient flow during the pandemic; (3) Changes to the staffing models related to the pandemic; (4) Use of personal protective equipment (PPE); (5) Changes in clinical practice and innovations; and (6) Current modalities of training including simulation.

RESULTS : We report on survey responses from 22 of 35 PICUs (63%). The majority of PICUs were located within children's hospitals (87%). All PICUs cared for pediatric patients with COVID-19 at the time of the survey. The majority of PICUs (83.4%) witnessed decreases in non-COVID-19 patients, 43% had COVID-19 dedicated units, and 74.6% pivoted to accept adult COVID-19 patients. All PICUs implemented changes to their staffing models with the most common changes being changes in COVID-19 patient room assignment in 50% of surveyed PICUs and introducing remote patient monitoring in 36% of the PICU units. Ninety-five percent of PICUs conducted training for donning and doffing of enhanced PPE. Even 6 months into the pandemic, one-third of PICUs across the United States reported shortages in PPE. The most common training formats for PPE were hands-on training (73%) and video-based content (82%). The most common concerns related to COVID-19 practice were changes in clinical protocols and guidelines (50%). The majority of PICUs implemented significant changes in their airway management (82%) and cardiac arrest management protocols in COVID-19 patients (68%). Simulation-based training was the most commonly utilized training modality (82%), whereas team training (73%) and team dynamics (77%) were the most common training objectives.

CONCLUSIONS : A substantial proportion of surveyed PICUs reported on large changes in their preparedness and training efforts before and during the pandemic. PICUs implemented broad strategies including modifications to staffing, PPE usage, workflow, and clinical practice, while using simulation as the preferred training modality. Further research is needed to advance the level of preparedness, support staff assuredness, and support deep learning about which preparedness actions were effective and what lessons are needed to improve PICU care and staff protection for the next COVID-19 patient waves.

Abulebda Kamal, Ahmed Rami A, Auerbach Marc A, Bona Anna M, Falvo Lauren E, Hughes Patrick G, Gross Isabel T, Sarmiento Elisa J, Barach Paul R


COVID-19, Pediatric intensive care unit, Practice innovations, Preparedness, Simulation, Training

General General

TW-SIR: time-window based SIR for COVID-19 forecasts.

In Scientific reports ; h5-index 158.0

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries---China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

Liao Zhifang, Lan Peng, Liao Zhining, Zhang Yan, Liu Shengzong


General General

A model to rate strategies for managing disease due to COVID-19 infection.

In Scientific reports ; h5-index 158.0

Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.

Wang Shiyan, Ramkrishna Doraiswami


General General

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.

In Cell systems

Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity-based memory. We find that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit peptides may not be robustly displayed by the major histocompatibility complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.

Liu Ge, Carter Brandon, Gifford David K


SARS-CoV-2, combinatorial optimization, haplotype, machine learning, major histocompatibility complex, peptide vaccine, population coverage, subunit, vaccine augmentation, vaccine evaluation

General General

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

In PloS one ; h5-index 176.0

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

Albadr Musatafa Abbas Abbood, Tiun Sabrina, Ayob Masri, Al-Dhief Fahad Taha, Omar Khairuddin, Hamzah Faizal Amri


General General

Study of the Novel AI-Powered Emotional Intelligence and Mindfulness App (Ajivar) for the College population during the COVID-19 Pandemic.

In JMIR formative research

BACKGROUND : Emotional Intelligence (EI) and mindfulness can impact the level of anxiety and depression that an individual experiences. These symptoms have been exacerbated in college students during the COVID-19 pandemic. AjivarTM is an application that utilizes artificial intelligence (AI) and machine learning (ML) to deliver personalized mindfulness and EI training.

OBJECTIVE : The main objective of this research study was to determine the effectiveness of delivering an EI curriculum and mindfulness techniques using an AI conversation platform, AjivarTM to improve symptoms of anxiety and depression during this pandemic.

METHODS : 95 subjects ages 18-29 years were recruited from a second semester freshmen of students. All participants completed the online TestWell inventory at the start and at the end of the 14 week semester. The comparison group (n=45) was given routine mental wellness instruction. The intervention group (n=50) were required to complete AjivarTM activities in addition to routine mental wellness instruction during the semester, which coincided with the onset of the COVID-19 pandemic. This group also completed assessments to evaluate for anxiety (Generalized Anxiety Disorder scale, GAD-7) and depression (Patient Health Questionnaire, PHQ-9).

RESULTS : Study participants were 19.81.9 years old, 28% males (27/95), and 60% Caucasian. No significant demographic differences existed between the comparison and intervention groups. Subjects in the intervention group interacted with AjivarTM for a mean of 14241168 minutes. There was a significant decrease in anxiety as measured by GAD-7 (11.471.85 at the start of the study compared to 6.271.44, P<0.01, at the end). There was a significant reduction in the symptoms of depression measured by PHQ-9 (10.692.04 vs. 6.692.41, P<0.01). Both the intervention and the comparison groups independently had significant improvements in pre-post TestWell inventory. The subgroups in the inventory for social awareness and spirituality showed significant improvement in the intervention group. In a group of participants (n=11) where GAD-7 was available during the onset of the COVID-19 pandemic, it showed an increase in anxiety (11.012.16 at the start to 13.031.34, P=0.23) in mid-March (onset of pandemic) to a significant decrease at the end of the study period (6.31.44, P<0.01).

CONCLUSIONS : It is possible to deliver EI and mindfulness training in a scalable way using the AjivarTM app during the COVID-19 pandemic resulting in improvements in anxiety, depression, and EI in the college population.


Sturgill Ronda, Martinasek Mary, Schmidt Trine, Goyal Raj


Cardiology Cardiology

Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era.

In Cardiology in the young

OBJECTIVE : This study investigated the impact of the Webinar on deep human learning of CHD.

MATERIALS AND METHODS : This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design.

RESULTS : One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they "agreed" or "strongly agreed" that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar.

CONCLUSION : E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a "sustainable academia".

McMahon Colin J, Tretter Justin T, Faulkner Theresa, Krishna Kumar R, Redington Andrew N, Windram Jonathan D


COVID-19, Conference, Webinar, e-learning, education, tetralogy of Fallot

General General

Machine Learning Models for covid-19 future forecasting.

In Materials today. Proceedings

Computational methods for machine learning (ML) have shown their meaning for the projection of potential results for informed decisions. Machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. This study shows the ability to predict the number of individuals who are affected by the COVID-19[1] as a potential threat to human beings by ML modelling. In this analysis, the risk factors of COVID-19 were exponential smoothing (ES). The Lower Absolute Reductor and Selection Operator, (LASSo), Vector Assistance (SVM), four normal potential forecasts, such as Linear Regression (LR)). [2] Each of these machine-learning models has three distinct kinds of predictions: the number of newly infected COVID 19 people, mortality rates and the recovered COVID-19 estimates in the next 10 days. These approaches are better used in the latest COVID-19 situation, as shown by the findings of the analysis. The LR, that is effective in predicting new cases of corona, death numbers and recovery.

Mojjada Ramesh Kumar, Yadav Arvind, Prabhu A V, Natarajan Yuvaraj


COVID-19, R2 score adjusted, exponential process of smoothing, future forecasting, machine learning supervised

General General

Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems - Evidence from the selected countries.

In Results in physics

Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computer-time and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the high-accuracy matching of the forecast model with real data.

Vyklyuk Yaroslav, Manylich Mykhailo, Škoda Miroslav, Radovanović Milan M, Petrović Marko D


COVID-19, forecasting, modified multi-agent systems, public activities, simulations

General General

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.

In Machine learning

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

van der Schaar Mihaela, Alaa Ahmed M, Floto Andres, Gimson Alexander, Scholtes Stefan, Wood Angela, McKinney Eoin, Jarrett Daniel, Lio Pietro, Ercole Ari


COVID-19, Clinical decision support, Healthcare

General General

Information Technology Solutions, Challenges, and Suggestions for Tackling the COVID-19 Pandemic.

In International journal of information management

Various technology innovations and applications have been developed to fight the coronavirus pandemic. The pandemic also has implications for the design, development, and use of technologies. There is an urgent need for a greater understanding of what roles information systems and technology researchers can play in this global pandemic. This paper examines emerging technologies used to mitigate the threats of COVID-19 and relevant challenges related to technology design, development, and use. It also provides insights and suggestions into how information systems and technology scholars can help fight the COVID-19 pandemic. This paper helps promote future research and technology development to produce better solutions for tackling the COVID-19 pandemic and future pandemics.

He Wu, Zhang Justin, Li Wenzhuo


Artificial Intelligence, Big Data, Blockchain, COVID-19, Digital Divide, Human Behavior, Information Systems, System Integration

General General

Patients' perceptions of teleconsultation during COVID-19: A cross-national study.

In Technological forecasting and social change

In recent months, humanity has had to deal with a worldwide pandemic called COVID-19, which has caused the death of hundreds of thousands of people and paralyzed the global economy. Struggling to cure infected patients while continuing to care for patients with other pathologies, health authorities have faced the lack of medical staff and infrastructure. This study aimed to investigate the acceptance of teleconsultation solutions by patients, which help to avoid the spread of the disease during this pandemic period. The model was built using some constructs of the technology acceptance model UTAUT2, Personal traits, Availability, and Perceived Risks. A new scale on Contamination Avoidance was developed by the authors. The questionnaire was disseminated in several countries in Europe and Asia and a total sample of 386 respondents was collected. The results emphasize the huge impact of Performance Expectancy, the negative influence of Perceived Risk, and the positive influence of Contamination Avoidance on the adoption of teleconsultation solutions. The findings highlight the moderating effects of Age, Gender, and Country.

Baudier Patricia, Kondrateva Galina, Ammi Chantal, Chang Victor, Schiavone Francesco


Acceptance, COVID-19, Pandemic, Teleconsultation, Telemedicine

Public Health Public Health

An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses.

In Journal of biomedical informatics ; h5-index 55.0

Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

Wen Andrew, Wang Liwei, He Huan, Liu Sijia, Fu Sunyang, Sohn Sunghwan, Kugel Jacob A, Kaggal Vinod C, Huang Ming, Wang Yanshan, Shen Feichen, Fan Jungwei, Liu Hongfang


COVID-19, Deep Learning, Syndromic Surveillance

Public Health Public Health

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

In The Science of the total environment

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed. In this study, the first attempted COVID-19 case predicting model based on county-level demographic, environmental, and mobility data was constructed with multiple machine learning techniques and a hybrid framework. Different scenarios were also applied to selected metropolitan counties including New York City, Cook County in Illinois, Los Angeles County in California, and Miami-Dade County in Florida to assess the impact from lockdown, Phase I, and Phase III re-opening. Our results showed that, for selected counties, the mobility decreased substantially after the lockdown but kept increasing with an apparent weekly pattern, and the weekly pattern of mobility and infections implied high infections during the weekend. Meanwhile, our model was successfully built up, and the scenario assessment results indicated that, compared with Phase I re-opening, a 1-week and a 2-week lockdown could reduce 4%-29% and 15%-55% infections, respectively, in the future week, while 2-week Phase III re-opening could increase 16%-80% infections. We concluded that the mandatory orders in metropolitan counties such lockdown should last longer than one week, the effect could be observed. The impact of lockdown or re-opening was also county-dependent and varied with the local pandemic. In future works, we expect to involve a longer period of data, consider more county-dependent factors, and employ more sophisticated techniques to decrease the modeling uncertainty and apply it to counties nationally and other countries.

Kuo Cheng-Pin, Fu Joshua S


County-level, Forecasting, Lockdown, Pandemic, Re-opening

General General

Corticosteroid therapy is associated with improved outcome in critically ill COVID-19 patients with hyperinflammatory phenotype.

In Chest ; h5-index 81.0

BACKGROUND : Corticosteroid therapy is commonly used in patients with coronavirus disease 2019 (COVID-19), while its impact on outcomes and which patients could benefit from corticosteroid therapy are uncertain.

RESEARCH QUESTION : Whether clinical phenotypes of COVID-19 were associated with differential response to corticosteroid therapy.

STUDY DESIGN AND METHODS : Critically ill patients with COVID-19 from Tongji hospital between Jan 2020 and Feb 2020 were included, and the main exposure of interest was the administration of intravenous corticosteroids. The primary outcome was 28-day mortality. Marginal structural modeling was used to account for baseline and time-dependent confounders. An unsupervised machine learning approach was carried out to identify phenotypes of COVID-19.

RESULTS : A total of 428 patients were included, and 280/428 (65.4%) patients received corticosteroid therapy. The 28-day mortality was significantly higher in patients who received corticosteroid therapy than in those who did not (53.9% vs. 19.6%; p<0.0001). After marginal structural modeling, corticosteroid therapy was not significantly associated with 28-day mortality (HR 0.80, 95% CI 0.54-1.18; p=0.26). Our analysis identified two phenotypes of COVID-19, and compared to the hypoinflammatory phenotype, the hyperinflammatory phenotype was characterized by elevated levels of proinflammatory cytokines, higher SOFA scores and higher rates of complications. Corticosteroid therapy was associated with a reduced 28-day mortality (HR 0.45; 95% CI 0.25-0.80; p=0.0062) in patients with hyperinflammatory phenotype.

INTERPRETATION : For critically ill patients with COVID-19, corticosteroid therapy was not associated with 28-day mortality, but the use of corticosteroids showed significant survival benefits in patients with the hyperinflammatory phenotype.

Chen Hui, Xie Jianfeng, Su Nan, Wang Jun, Sun Qin, Li Shusheng, Jin Jun, Zhou Jing, Mo Min, Wei Yao, Chao Yali, Hu Weiwei, Du Bin, Qiu Haibo


COVID-19, Corticosteroid, Phenotype

General General

Correction: Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis.

In Journal of medical Internet research ; h5-index 88.0

[This corrects the article DOI: 10.2196/21329.].

Alanazi Eisa, Alashaikh Abdulaziz, Alqurashi Sarah, Alanazi Aued


Radiology Radiology

Chest CT imaging features and severity scores as biomarkers for prognostic prediction in patients with COVID-19.

In Annals of translational medicine

Background : Coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have explored the role of chest computed tomography (CT) features and severity scores for prognostic prediction. In this study, we aimed to investigate the role of chest CT severity score and imaging features in the prediction of the prognosis of COVID-19 patients.

Methods : A total of 134 patients (62 recovered and 72 deceased patients) with confirmed COVID-19 were enrolled. The clinical, laboratory, and chest CT (316 scans) data were retrospectively reviewed. Demographics, symptoms, comorbidities, and temporal changes of laboratory results, CT features, and severity scores were compared between recovered and deceased groups using the Mann-Whitney U test and logistic regression to identify the risk factors for poor prognosis.

Results : Median age was 48 and 58 years for recovered and deceased patients, respectively. More patients had at least one comorbidity in the deceased group than the recovered group (60% vs. 29%). Leukocytes, neutrophil, high-sensitivity C-reactive protein (hsCRP), prothrombin, D-dimer, serum ferritin, interleukin (IL)-2, and IL-6 were significantly elevated in the deceased group than the recovered group at different stages. The total CT score at the peak stage was significantly greater in the deceased group than the recovered group (20 vs. 11 points). The optimal cutoff value of the total CT scores was 16.5 points, achieving 69.4% sensitivity and 82.2% specificity for the prognostic prediction. The crazy-paving pattern and consolidation were more common in the deceased patients than those in the recovered patients. Linear opacities significantly increased with the disease course in the recovered patients. Sex, age, neutrophil, IL-2, IL-6, and total CT scores were independent risk factors for the prognosis with odds ratios of 3.8 to 8.7.

Conclusions : Sex (male), older age (>60 years), elevated neutrophil, IL-2, IL-6 level, and total CT scores (≥16) were independent risk factors for poor prognosis in patients with COVID-19. Temporal changes of chest CT features and severity scores could be valuable for early identification of severe cases and eventually reducing the mortality rate of COVID-19.

Zhou Shuchang, Chen Chengyang, Hu Yiqi, Lv Wenzhi, Ai Tao, Xia Liming


Coronavirus disease, computed tomography, prognosis, risk factor, severity score

General General

Asynchrony Between Individual and Government Actions Accounts for Disproportionate Impact of COVID-19 on Vulnerable Communities.

In American journal of preventive medicine ; h5-index 75.0

INTRODUCTION : Previously estimated effects of social distancing do not account for changes in individual behavior before the implementation of stay-at-home policies or model this behavior in relation to the burden of disease. This study aims to assess the asynchrony between individual behavior and government stay-at-home orders, quantify the true impact of social distancing using mobility data, and explore the sociodemographic variables linked to variation in social distancing practices.

METHODS : This study was a retrospective investigation that leveraged mobility data to quantify the time to behavioral change in relation to the initial presence of COVID-19 and the implementation of government stay-at-home orders. The impact of social distancing that accounts for both individual behavior and testing data was calculated using generalized mixed models. The role of sociodemographics in accounting for variation in social distancing behavior was modeled using a 10-fold cross-validated elastic net (linear machine learning model). Analysis was conducted in April‒July 2020.

RESULTS : Across all the 1,124 counties included in this analysis, individuals began to socially distance at a median of 5 days (IQR=3-8) after 10 cumulative cases of COVID-19 were confirmed in their state, with state governments taking a median of 15 days (IQR=12-19) to enact stay-at-home orders. Overall, people began social distancing at a median of 12 days (IQR=8-17) before their state enacted stay-at-home orders. Of the 16 studies included in the review, 13 exclusively used government dates as a proxy for social distancing behavior, and none accounted for both testing and mobility. Using government stay-at-home dates as a proxy for social distancing (10.2% decrease in the number of daily cases) accounted for only 55% of the true impact of the intervention when compared with estimates using mobility (18.6% reduction). Using 10-fold cross-validation, 23 of 43 sociodemographic variables were significantly and independently predictive of variation in individual social distancing, with delays corresponding to an increase in a county's proportion of people without a high school diploma and proportion of racial and ethnic minorities.

CONCLUSIONS : This retrospective analysis of mobility patterns found that social distancing behavior occurred well before the onset of government stay-at-home dates. This asynchrony leads to the underestimation of the impact of social distancing. Sociodemographic characteristics associated with delays in social distancing can help explain the disproportionate case burden and mortality among vulnerable communities.

Abdalla Moustafa, Abar Arjan, Beiter Evan R, Saad Mohamed


General General

Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data

ArXiv Preprint

As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim to develop risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this paper, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.

Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi, Yan Liu


Public Health Public Health

Access denied: the shortage of digitized fitness resources for people with disabilities.

In Disability and rehabilitation ; h5-index 45.0

PURPOSE : The COVID-19 pandemic has drastically impacted every aspect of life, including how people exercise and access fitness resources. Prior to COVID-19, the global burden of disease attributable to sedentary behavior disproportionately affected the health of people with disabilities (PWD). This pre-existing gap has only widened during COVID-19 due to limited disability-friendly digital exercise resources. The purpose of this work is to examine this gap in accessibility to digital fitness resources, and re-frame the notion of accessibility to suit the contemporary context.

MATERIALS AND METHODS : Using machine learning, video titles/descriptions about home exercise ordered by relevance populated on YouTube between 1 January 2020 and 30 June 2020 were examined.

RESULTS : Using the search terms, "home exercise," "home-based exercise," "exercise no equipment," "workout no equipment," "exercise at home," or "at-home exercise," 700 videos ordered by relevance included 28 (4%) that were inclusive of participants with disabilities. Unfortunately, most digital fitness resources are therefore inaccessible to PWD. The global pause the pandemic has induced may be the right moment to construct a comprehensive, indexed digital library of home-based fitness video content for the disabled. There is a further need for more nuanced understandings of accessibility as technological advancements continue. Implications for Rehabilitation Physical activity is incredibly important to the quality of life and health of all people. Physical activity levels, however, remain lower among persons with disabilities. Access to disability-friendly resources remains a challenge and worsened by the circumstances of COVID-19 due to an apparent lack of digital fitness resources for persons with disabilities. A broader and comprehensive definition of accessibility must recognize digital advances and access to physical activity for persons with disabilities must feature digital resources.

Stratton Catherine, Kadakia Shevali, Balikuddembe Joseph K, Peterson Mark, Hajjioui Abderrazak, Cooper Rory, Hong Bo-Young, Pandiyan Uma, Muñoz-Velasco Laura Paulina, Joseph James, Krassioukov Andrei, Tripathi Deo Rishi, Tuakli-Wosornu Yetsa A


Accessibility, digital resources, home exercise, inclusive, people with disabilities

Public Health Public Health

Ruling In and Ruling Out COVID-19: Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging and Test Data.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care.

OBJECTIVE : Develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling-in and ruling-out COVID-19 in potential patients. This study compares the diagnostic performance of probabilistic, graphical, and machine-learning models against a previously published benchmark model.

METHODS : We integrated patient symptom and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on thirteen symptoms, estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19 compatible illness at the University of California San Diego Medical Center over 14 days starting in March 2020.

RESULTS : We included 55 consecutive patients with fever (78%) or cough (77%) presenting for ambulatory (n=11) or hospital care (n=44). 51% (n=28) were female, 49% were age <60. Common comorbidities included diabetes (22%), hypertension (27%), cancer (16%) and cardiovascular disease (13%). 69% of these (n=38) were RT-PCR confirmed positive for SARS-CoV-2 infection, 11 had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric-learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6 - 84.2%, specificities of 58.8 - 70.6%, and accuracies of 61.4 - 71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices.

CONCLUSIONS : Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real world settings.


D’Ambrosia Christopher, Christensen Henrik, Aronoff-Spencer Eliah


Radiology Radiology

Artificial intelligence can predict the mortality of COVID-19 patients at the admission time using routine blood samples.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments.

OBJECTIVE : To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet, to predict in-hospital mortality using a routine blood sample at the time of hospital admission.

METHODS : We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining a deep neural network and random forest model. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions.

RESULTS : In the testing datasets, EDRnet provided high sensitivity (100%), specificity (91.35%), and accuracy (91.51%). To extend the number of patient data, we developed a web application (, where anyone can access the model to predict the mortality and can register his or her own blood laboratory results.

CONCLUSIONS : Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help healthcare providers fight COVID1-19 and improve patients' outcome.


Ko Hoon, Chung Heewon, Kang Wu Seong, Park Chul, Kim Do Wan, Kim Seong Eun, Chung Chi Ryang, Ko Ryoung Eun, Lee Hooseok, Seo Jae Ho, Choi Tae-Young, Jaimes Rafael, Kim Kyung Won, Lee Jinseok


General General

Hybrid-COVID: a novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images.

In Physical and engineering sciences in medicine

The novel Coronavirus disease (COVID-19), which first appeared at the end of December 2019, continues to spread rapidly in most countries of the world. Respiratory infections occur primarily in the majority of patients treated with COVID-19. In light of the growing number of COVID-19 cases, the need for diagnostic tools to identify COVID-19 infection at early stages is of vital importance. For decades, chest X-ray (CXR) technologies have proven their ability to accurately detect respiratory diseases. More recently, with the availability of COVID-19 CXR scans, deep learning algorithms have played a critical role in the healthcare arena by allowing radiologists to recognize COVID-19 patients from their CXR images. However, the majority of screening methods for COVID-19 reported in recent studies are based on 2D convolutional neural networks (CNNs). Although 3D CNNs are capable of capturing contextual information compared to their 2D counterparts, their use is limited due to their increased computational cost (i.e. requires much extra memory and much more computing power). In this study, a transfer learning-based hybrid 2D/3D CNN architecture for COVID-19 screening using CXRs has been developed. The proposed architecture consists of the incorporation of a pre-trained deep model (VGG16) and a shallow 3D CNN, combined with a depth-wise separable convolution layer and a spatial pyramid pooling module (SPP). Specifically, the depth-wise separable convolution helps to preserve the useful features while reducing the computational burden of the model. The SPP module is designed to extract multi-level representations from intermediate ones. Experimental results show that the proposed framework can achieve reasonable performances when evaluated on a collected dataset (3 classes to be predicted: COVID-19, Pneumonia, and Normal). Notably, it achieved a sensitivity of 98.33%, a specificity of 98.68% and an overall accuracy of 96.91.

Bayoudh Khaled, Hamdaoui Fayçal, Mtibaa Abdellatif


COVID-19, Chest X-ray, Deep learning, Hybrid 2D/3D CNN, Pneumonia

General General

Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection.

In JAMA oncology ; h5-index 85.0

Importance : Patients with specific cancers may be at higher risk than those without cancer for coronavirus disease 2019 (COVID-19) and its severe outcomes. At present, limited data are available on the risk, racial disparity, and outcomes for COVID-19 illness in patients with cancer.

Objectives : To investigate how patients with specific types of cancer are at risk for COVID-19 infection and its adverse outcomes and whether there are cancer-specific race disparities for COVID-19 infection.

Design, Setting, and Participants : This retrospective case-control analysis of patient electronic health records included 73.4 million patients from 360 hospitals and 317 000 clinicians across 50 US states to August 14, 2020. The odds of COVID-19 infections for 13 common cancer types and adverse outcomes were assessed.

Exposures : The exposure groups were patients diagnosed with a specific cancer, whereas the unexposed groups were patients without the specific cancer.

Main Outcomes and Measures : The adjusted odds ratio (aOR) and 95% CI were estimated using the Cochran-Mantel-Haenszel test for the risk of COVID-19 infection.

Results : Among the 73.4 million patients included in the analysis (53.6% female), 2 523 920 had at least 1 of the 13 common cancers diagnosed (all cancer diagnosed within or before the last year), and 273 140 had recent cancer (cancer diagnosed within the last year). Among 16 570 patients diagnosed with COVID-19, 1200 had a cancer diagnosis and 690 had a recent cancer diagnosis of at least 1 of the 13 common cancers. Those with recent cancer diagnosis were at significantly increased risk for COVID-19 infection (aOR, 7.14 [95% CI, 6.91-7.39]; P < .001), with the strongest association for recently diagnosed leukemia (aOR, 12.16 [95% CI, 11.03-13.40]; P < .001), non-Hodgkin lymphoma (aOR, 8.54 [95% CI, 7.80-9.36]; P < .001), and lung cancer (aOR, 7.66 [95% CI, 7.07-8.29]; P < .001) and weakest for thyroid cancer (aOR, 3.10 [95% CI, 2.47-3.87]; P < .001). Among patients with recent cancer diagnosis, African Americans had a significantly higher risk for COVID-19 infection than White patients; this racial disparity was largest for breast cancer (aOR, 5.44 [95% CI, 4.69-6.31]; P < .001), followed by prostate cancer (aOR, 5.10 [95% CI, 4.34-5.98]; P < .001), colorectal cancer (aOR, 3.30 [95% CI, 2.55-4.26]; P < .001), and lung cancer (aOR, 2.53 [95% CI, 2.10-3.06]; P < .001). Patients with cancer and COVID-19 had significantly worse outcomes (hospitalization, 47.46%; death, 14.93%) than patients with COVID-19 without cancer (hospitalization, 24.26%; death, 5.26%) (P < .001) and patients with cancer without COVID-19 (hospitalization, 12.39%; death, 4.03%) (P < .001).

Conclusions and Relevance : In this case-control study, patients with cancer were at significantly increased risk for COVID-19 infection and worse outcomes, which was further exacerbated among African Americans. These findings highlight the need to protect and monitor patients with cancer as part of the strategy to control the pandemic.

Wang QuanQiu, Berger Nathan A, Xu Rong


General General

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.

In Journal of biomolecular structure & dynamics

To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019-May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody development for COVID-19. Out of 31 studies included, 16 studies applied AI for drug repurposing, whereas 10 studies utilized AI for novel drug discovery. Only four studies used AI technology for vaccine development, whereas one study generated stable antibodies against SARS-CoV-2. Approx. 50% of studies exclusively targeted 3CLpro of SARS-CoV-2, and only two studies targeted ACE/TMPSS2 for inhibiting host viral interactions. Around 16% of the identified drugs are in different phases of clinical evaluation against COVID-19. AI has emerged as a promising solution of COVID-19 therapeutics. During this current pandemic, many of the researchers have used AI-based strategies to process large databases in a more customized manner leading to the faster identification of several potential targets, novel/repurposing of drugs and vaccine candidates. A number of these drugs are either approved or are in a late-stage clinical trial and are potentially effective against SARS-CoV2 indicating validity of the methodology. However, as the use of AI-based screening program is currently in budding stage, sole reliance on such algorithms is not advisable at this current point of time and an evidence based approach is warranted to confirm their usefulness against this life-threatening disease. Communicated by Ramaswamy H. Sarma.

Kaushal Karanvir, Sarma Phulan, Rana S V, Medhi Bikash, Naithani Manisha


Artificial intelligence, COVID-19, drug repurposing, novel drug discovery, vaccine development

Public Health Public Health

Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.

In Scientific reports ; h5-index 158.0

Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66-1.92]), male sex (OR, 1.57 [95% CI 1.30-1.90]), higher BMI (OR, 1.03 [95% CI 1.102-1.05]), higher heart rate (OR, 1.01 [95% CI 1.00-1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Rechtman Elza, Curtin Paul, Navarro Esmeralda, Nirenberg Sharon, Horton Megan K


General General

Coronavirus Disease 2019: Virology and Drug Targets.

In Infectious disorders drug targets

The Coronavirus Disease 2019, a pandemic caused by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is seriously affecting global health and the economy. As the vaccine development takes time, the current research is focused on repurposing FDA approved drugs against the viral target proteins. This review discusses the current understanding of SARS-CoV-2 virology, its target structural proteins (S- glycoprotein), non-structural proteins (3- chymotrypsin-like protease, papain-like protease, RNA-dependent RNA polymerase, and helicase) and accessory proteins, drug discovery strategies (drug repurposing, artificial intelligence, and high-throughput screening), and the current status of antiviral drug development.

Krishnamurthy Praveen Thaggikuppe


CoVID-19, Drug targets, SARS-CoV-2, Virology

General General

Interpretable detection of novel human viruses from genome sequencing data

bioRxiv Preprint

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

Bartoszewicz, J. M.; Seidel, A.; Renard, B. Y.


Public Health Public Health

On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic.

MATERIALS AND METHODS : The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications.

RESULTS : The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states.

CONCLUSION : These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

Bednarski Bryan P, Singh Akash Deep, Jones William M


Allocation, Artificial Intelligence, Coronavirus, Machine Learning, Resource

Radiology Radiology

Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

ArXiv Preprint

Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid-19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.

Ali Narin


General General

Application of artificial neural networks to predict the COVID-19 outbreak.

In Global health research and policy

BACKGROUND : Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers.

METHODS : The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered.

RESULTS : Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries.

CONCLUSION : Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

Niazkar Hamid Reza, Niazkar Majid


Artificial intelligence, Artificial neural network, COVID-19 outbreak, Estimation model, Prediction model, SARS-COV-2

Radiology Radiology

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Diagnosis and Severity Assessment of COVID-19

ArXiv Preprint

The outbreak of COVID-19 has resulted in over 67 million infections with over 1.5 million deaths worldwide so far. Both computer tomography (CT) diagnosis and nucleic acid test (NAT) have their pros and cons. Here we present a multitask-learning (MTL) framework, termed COVID-MTL, which is capable of simultaneously detecting COVID-19 against both radiology and NAT as well as assessing infection severity. We proposed an active-contour based method to refine lung segmentation results on COVID-19 CT scans and a Shift3D real-time 3D augmentation algorithm to improve the convergence and accuracy of state-of-the-art 3D CNNs. A random-weighted multitask loss function was then proposed, which made simultaneous learning of different COVID-19 tasks more stable and accurate. By only using CT data and extracting lung imaging features, COVID-MTL was trained on 930 CT scans and tested on another 399 cases, which yielded AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, and outperformed state-of-the-art models. COVID-MTL yielded AUC of 0.800 $\pm$ 0.020 and 0.813 $\pm$ 0.021 (with transfer learning) for classifying control/suspected (AUC of 0.841), mild/regular (AUC of 0.808), and severe/critically-ill (AUC of 0.789) cases. Besides, we identified top imaging biomarkers that are significantly related (P < 0.001) to the positivity and severity of COVID-19.

Guoqing Bao, Xiuying Wang


General General

The Effect of COVID-19 Residential Lockdown on Subjective Well-Being in China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The residential lockdowns were implemented in quite a few cities in China to contain the rapid spread of Corona Virus Disease 2019 (COVID-19). Although the excessively stringent regulation effectively slowed the spread of the disease, it might have challenged the well-being of the residents.

OBJECTIVE : This study aims to explore the effect of the residential lockdown on subjective well-being (SWB) of individuals during COVID-19.

METHODS : The sample consisted of 1,790 lockdown residents (73.18% female) and 3,580 non-lockdown residents (gender matched with 1,790 lockdown residents) on Sina Weibo. In both the lockdown and non-lockdown groups, we calculated the SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown, using individuals' original posts on Sina Weibo. This calculation of SWB was via online ecological recognition (OER), which was based on established machine-learning predictive models.

RESULTS : The time (before the residential lockdown, after the residential lockdown) × area (lockdown, non-lockdown) interactions in integral analysis (N = 5370) showed that after the residential lockdown, compared with non-lockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F(1, 5368) = 13.593, P < .001) and paranoid ideation (F(1, 5368) = 14.333, P < .001). The time (before the residential lockdown, after the residential lockdown) × area (developed, under-developed) interactions in the comparison of the residential lockdown areas with different economic development (N = 1790) indicated that the SWB of residents in under-developed areas showed no significant change after the residential lockdown (P > .05) while those in developed areas changed.

CONCLUSIONS : These findings increase the understanding of the psychological impact and cost of residential lockdown during the epidemic. The more negative changes in residents' SWB in developed areas imply greater demand of psychological intervention under residential lockdown.

Wang Yilin, Wu Peijing, Liu Xiaoqian, Li Sijia, Zhu Tingshao, Zhao Nan


General General

Repurposing potential of FDA approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

In Chemical biology & drug design ; h5-index 32.0

The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body. To evaluate molecular affinities between antiviral drug candidates and SARS-CoV-2 associated target proteins such as spike protein (S) and main protease (Mpro ), a molecular interaction simulation was performed using MD software and subsequently the applicability score was calculated by machine learning algorithms. Furthermore, the STITCH algorithm was used to predict the pharmacology network involving multiple pathways of active drug candidate(s). Pharmacophores feature of active drug(s) molecules was also determined to predict structure activity relationship. The molecular interaction analysis showed that cordycepin has strong binding affinities with S protein (-180) and Mpro proteins (-205) which were relatively highest among other drug candidates used. Interestingly, compounds with low IC50 showed high binding energy. Furthermore, machine learning algorithm also revealed high applicability scores (0.42-0.47) of cordycepin. It is worth mentioning that the pharmacology network depicted the involvement of cordycepin in different pathways associated with bacterial and viral diseases including tuberculosis, hepatitis B, influenza A, viral myocarditis and herpes simplex infection. The embedded pharmacophore features with cordycepin also suggested strong structure-activity relationship (SAR). Cordycepin's anti-SARS-CoV-2 activity indicated 65% (E-gene) and 42% (N-gene) viral replication inhibition after 48h of treatment. Since cordycepin has both pre-clinical and clinical evidence on antiviral activity, in addition the present findings further validate and suggest repurposing potential of cordycepin against COVID-19.

Verma Akalesh K, Aggarwal Rohit


2019nCov, antiviral drugs, cordycepin, coronavirus, drug repurposing

General General

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features.

In Entropy (Basel, Switzerland)

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

Hasan Ali M, Al-Jawad Mohammed M, Jalab Hamid A, Shaiba Hadil, Ibrahim Rabha W, Al-Shamasneh Ala’a R


CT scans of lungs, LSTM network, Q—deformed entropy, deep learning, features extraction, fractional calculus

Radiology Radiology

Hypergraph learning for identification of COVID-19 with CT imaging.

In Medical image analysis

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

Di Donglin, Shi Feng, Yan Fuhua, Xia Liming, Mo Zhanhao, Ding Zhongxiang, Shan Fei, Song Bin, Li Shengrui, Wei Ying, Shao Ying, Han Miaofei, Gao Yaozong, Sui He, Gao Yue, Shen Dinggang


COVID-19 pneumonia, Hypergraph learning, Uncertainty calculation, Vertex-weighted

General General

COVID-AL: The diagnosis of COVID-19 with deep active learning.

In Medical image analysis

The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy, which simultaneously considers sample diversity and predicted loss. With a tailor-designed 3D residual network, the proposed COVID-AL can diagnose COVID-19 efficiently and it is validated on a large CT scan dataset collected from the CC-CCII. The experimental results demonstrate that the proposed COVID-AL outperforms the state-of-the-art active learning approaches in the diagnosis of COVID-19. With only 30% of the labeled data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed COVID-AL framework.

Wu Xing, Chen Cheng, Zhong Mingyu, Wang Jianjia, Shi Jun


COVID-19, Computer-aided diagnosis, Deep active learning, Predicted loss, Sample diversity

Public Health Public Health

Artificial Intelligence in the Fight against COVID-19: A Scoping Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In December 2019, the novel Coronavirus disease (COVID-19) broke out in Wuhan, China leading to major national and international disruptions in healthcare, business, education, transportation, and nearly every aspect of our daily lives. Artificial Intelligence (AI) has been leveraged amid the COVID-19 pandemic, however, little is known about its use for supporting public health efforts.

OBJECTIVE : The scoping review aimed to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is first review that describes and summarizes features of the identified AI techniques and datasets used for their development and validation.

METHODS : A scoping review was conducted following the guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched the most commonly used electronic databases (e.g., MEDLINE, EMBASE, PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS : We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing, and assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts/reservoirs. Researchers utilized AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and length of hospital stay. AI was used for Infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI techniques used were Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM).

CONCLUSIONS : The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Abd-Alrazaq Alaa, Alajlani Mohannad, Alhuwail Dari, Schneider Jens, Al-Kuwari Saif, Shah Zubair, Hamdi Mounir, Househ Mowafa


Cardiology Cardiology

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China.

In Engineering (Beijing, China)

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

Yuan Ye, Sun Chuan, Tang Xiuchuan, Cheng Cheng, Mombaerts Laurent, Wang Maolin, Hu Tao, Sun Chenyu, Guo Yuqi, Li Xiuting, Xu Hui, Ren Tongxin, Xiao Yang, Xiao Yaru, Zhu Hongling, Wu Honghan, Li Kezhi, Chen Chuming, Liu Yingxia, Liang Zhichao, Cao Zhiguo, Zhang Hai-Tao, Ch Paschaldis Ioannis, Liu Quanying, Goncalves Jorge, Zhong Qiang, Yan Li


COVID-19, Mortality risk prediction, Risk score

General General

How chemical engineers can contribute to fight the COVID-19.

In Journal of the Taiwan Institute of Chemical Engineers

The SARS-CoV-2 virus, promoter of COVID-19, already infected millions of people around the world, resulting in thousands of fatal victims. Facing this unprecedented crisis in human history, several research groups, industrial companies and governments have been spending efforts to develop vaccines and medications. People from distinct knowledge fields are doing their part in order to overcome this crisis. Chemical Engineers are also contributing in the development of actions to control the SARS-CoV-2 virus. However, many chemical engineers still do not know how to use the knowledge acquired from Chemical Engineering school to collaborate in the fight against the COVID-19. In this context, the present paper aims to discuss several knowledge fields within the Chemical Engineering and correlated areas successfully applied to create innovative and effective solutions in the fight against the COVID-19.

Santana Harrson S, de Souza Marcos R P, Lopes Mariana G M, Souza Johmar, Silva Renan R O, Palma Mauri S A, Nakano Wilson L V, Lima Giovanni A S, Munhoz Guadalupe, Noriler Dirceu, Taranto Osvaldir P, Silva João L


Artificial intelligence, COVID19, Fluid dynamics, Microfluidics, SARS-CoV-2, Vaccine

General General

Data Analysis of Covid-19 Pandemic and Short-Term Cumulative Case Forecasting Using Machine Learning Time Series Models.

In Chaos, solitons, and fractals

The Covid-19 pandemic is the most important health disaster that has surrounded the world for the past eight months. There is no clear date yet on when it will end. By now, more than 31 million people have been infected worldwide. Predicting the Covid-19 trend has become a challenging issue. In this study, data of COVID-19 between 20/01/2020 and 18/09/2020 for USA, Germany and Global was obtained from World Health Organization. Dataset consist of weekly confirmed cases and weekly cumulative confirmed cases for 35 weeks. Then the distribution of the data was examined using the most up-to-date Covid-19 weekly case data and its parameters were obtained according to the statistical distributions. Furthermore, time series prediction model using machine learning was proposed to obtain the curve of disease and forecast the epidemic tendency. Linear regression, multi-layer perceptron, random forest and support vector machines (SVM) machine learning methods were used. The performances of the methods were compared according to the RMSE, APE, MAPE metrics and it was seen that SVM achieved the best trend. According to estimates, the global pandemic will peak at the end of January 2021 and estimated approximately 80 million people will be cumulatively infected.

Ballı Serkan


Covid-19, Machine Learning, Multi-layer perceptron, Statistical distribution, Support vector machine

General General

Prediction of COVID-19 Confirmed Cases Combining Deep Learning Methods and Bayesian Optimization.

In Chaos, solitons, and fractals

COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study, we propose three hybrid approaches for forecasting COVID-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (LSTM), and convolutional neural network (CNN) with the Bayesian optimization algorithm. All models are designed based on the multiple-output forecasting strategy, which allows the forecasting of the multiple time points. The Bayesian optimization method automatically selects the best hyperparameters for each model and enhances forecasting performance. Using the publicly available epidemical data acquired from Johns Hopkins University's Coronavirus Resource Center, we conducted our experiments and evaluated the proposed models against the benchmark model. The results of experiments exhibit the superiority of the deep learning models over the benchmark model both for short-term forecasting and long-horizon forecasting. In particular, the mean SMAPE of the best deep learning model is 0.25 for the short-term forecasting (10 days ahead). Also, for long-horizon forecasting, the best deep learning model obtains the mean SMAPE of 2.59.

Abbasimehr Hossein, Paki Reza


Bayesian optimization, CNN, COVID-19, Deep learning, LSTM, Multi-head attention

Radiology Radiology

Radiology indispensable for tracking COVID-19.

In Diagnostic and interventional imaging

With the rapid spread of COVID-19 worldwide, early detection and efficient isolation of suspected patients are especially important to prevent the transmission. Although nucleic acid testing of SARS-CoV-2 is still the gold standard for diagnosis, there are well-recognized early-detection problems including time-consuming in the diagnosis process, noticeable false-negative rate in the early stage and lacking nucleic acid testing kits in some areas. Therefore, effective and rational applications of imaging technologies are critical in aiding the screen and helping the diagnosis of suspected patients. Currently, chest computed tomography is recommended as the first-line imaging test for detecting COVID-19 pneumonia, which could allow not only early detection of the typical chest manifestations, but also timely estimation of the disease severity and therapeutic effects. In addition, other radiological methods including chest X-ray, magnetic resonance imaging, and positron emission computed tomography also show significant advantages in the detection of COVID-19 pneumonia. This review summarizes the applications of radiology and nuclear medicine in detecting and diagnosing COVID-19. It highlights the importance for these technologies to curb the rapid transmission during the pandemic, considering findings from special groups such as children and pregnant women.

Li Jingwen, Long Xi, Wang Xinyi, Fang Fang, Lv Xuefei, Zhang Dandan, Sun Yu, Hu Shaoping, Lin Zhicheng, Xiong Nian


Artificial intelligence, COVID-19, Magnetic resonance imaging, Positron emission tomography computed tomography., Tomography, X-ray computed

Public Health Public Health

Text mining approaches for dealing with the rapidly expanding literature on COVID-19.

In Briefings in bioinformatics

More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

Wang Lucy Lu, Lo Kyle


CORD-19, COVID-19, information extraction, information retrieval, natural language processing, question answering, shared tasks, summarization, text mining

General General

A review of COVID-19 biomarkers and drug targets: resources and tools.

In Briefings in bioinformatics

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.

Caruso Francesca P, Scala Giovanni, Cerulo Luigi, Ceccarelli Michele


General General

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

bioRxiv Preprint

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.

Mirabelli, C.; Wotring, J. W.; Zhang, C. J.; McCarty, S. M.; Fursmidt, R.; Frum, T.; Kadambi, N. S.; Amin, A. T.; O’Meara, T. R.; Pretto-Kernahan, C. D.; Spence, J. R.; Huang, J.; Alysandratos, K. D.; Kotton, D. N.; Handelman, S. K.; Wobus, C. E.; Weatherwax, K. J.; Mashour, G. A.; O’Meara, M. J.; Sexton, J. Z.


General General

Improving Clinical Document Understanding on COVID-19 Research with Spark NLP

ArXiv Preprint

Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events in addition to other commonly used clinical and biomedical entities. Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient. Third, the deep learning models used are more accurate than previously available, leveraging an integrated pipeline of state-of-the-art pretrained named entity recognition models, and improving on the previous best performing benchmarks for assertion status detection. We illustrate extracting trends and insights, e.g. most frequent disorders and symptoms, and most common vital signs and EKG findings, from the COVID-19 Open Research Dataset (CORD-19). The system is built using the Spark NLP library which natively supports scaling to use distributed clusters, leveraging GPUs, configurable and reusable NLP pipelines, healthcare specific embeddings, and the ability to train models to support new entity types or human languages with no code changes.

Veysel Kocaman, David Talby


General General

Techniques assisting peptide vaccine and peptidomimetic design. Sidechain exposure in the SARS-CoV-2 spike glycoprotein.

In Computers in biology and medicine

The aim of the present study is to discuss the design of peptide vaccines and peptidomimetics against SARS-COV-2, to develop and apply a method of protein structure analysis that is particularly appropriate to applying and discussing such design, and also to use that method to summarize some important features of the SARS-COV-2 spike protein sequence. A tool for assessing sidechain exposure in the SARS-CoV-2 spike glycoprotein is described. It extends to assessing accessibility of sidechains by considering several different three-dimensional structure determinations of SARS-CoV-2 and SARS-CoV-1 spike protein. The method is designed to be insensitive to a distance limit for counting neighboring atoms and the results are in good agreement with the physical chemical properties and exposure trends of the 20 naturally occurring sidechains. The spike protein sequence is analyzed with comment regarding exposable character. It includes studies of complexes with antibody elements and ACE2. These indicate changes in exposure at sites remote to those at which the antibody binds. They are of interest concerning design of synthetic peptide vaccines, and for peptidomimetics as a basis of drug discovery. The method was also developed in order to provide linear (one-dimensional) information that can be used along with other bioinformatics data of this kind in data mining and machine learning, potentially as genomic data regarding protein polymorphisms to be combined with more traditional clinical data.

Robson B


Accessibility, COVID-19, Conformation, Coronavirus, Disorder, Exposure, Glycosylation, SARS-CoV-2, Spike glycoprotein

General General

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network.

In IEEE journal of biomedical and health informatics

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification.

Jiang Yifan, Chen Han, Loew M H, Ko Hanseok


General General

Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.

In European review for medical and pharmacological sciences

Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs, more repurposed drugs should be recognized. Furthermore, technological advancements have been helpful in the battle against COVID-19. Machine intelligence technology can support this procedure by rapidly determining adequate and effective drugs against COVID-19 and by overcoming any barrier between a large number of repurposed drugs, laboratory/clinical testing, and final drug authorization. This paper reviews the proposed vaccines and medicines for SARS-CoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.

Alsharif M H, Alsharif Y H, Albreem M A, Jahid A, Solyman A A A, Yahya K, Alomari O A, Hossain M S


General General

StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.

In Physical and engineering sciences in medicine

The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection.

Autee Pratik, Bagwe Sagar, Shah Vimal, Srivastava Kriti


Covid-19, Deep neural networks, Generative adversarial networks, Image segmentation, Stacked generalization, Transfer learning

General General

An Artificial Intelligence-Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan: Protocol for a Randomized Controlled Trial.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI.

OBJECTIVE : The primary objectives of this study are to evaluate whether a personalized mobile app can improve children's on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone-based app on vaccination improvement.

METHODS : A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children's 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers' perceptions about RCI and a mobile phone-based app in improving RCI coverage.

RESULTS : Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age.

CONCLUSIONS : This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs.



Kazi Abdul Momin, Qazi Saad Ahmed, Khawaja Sadori, Ahsan Nazia, Ahmed Rao Moueed, Sameen Fareeha, Khan Mughal Muhammad Ayub, Saqib Muhammad, Ali Sikander, Kaleemuddin Hussain, Rauf Yasir, Raza Mehreen, Jamal Saima, Abbasi Munir, Stergioulas Lampros K


AI, EPI, LMICs, Pakistan, artificial intelligence, mHealth, personalized messages, routine childhood immunization, routine immunization, smartphone apps, vaccine-preventable illnesses

General General

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

In Scientific reports ; h5-index 158.0

The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.

Sun Jichao, Chen Xi, Zhang Ziheng, Lai Shengzhang, Zhao Bo, Liu Hualuo, Wang Shuojia, Huan Wenjing, Zhao Ruihui, Ng Man Tat Alexander, Zheng Yefeng


Internal Medicine Internal Medicine

What Can COVID-19 Teach Us about Using AI in Pandemics?

In Healthcare (Basel, Switzerland)

The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI's weaknesses as key factors affecting AI in future pandemic preparedness and response.

Laudanski Krzysztof, Shea Gregory, DiMeglio Matthew, Rastrepo Mariana, Solomon Cassie


COVID-19, artificial intelligence, demand constraints, innovation, pandemic

General General

Psychological screening and tracking of athletes and the potential for digital mental health solutions in a hybrid model of care: A mini review.

In JMIR formative research

BACKGROUND : There is a persistent need for mental ill-health prevention and intervention among 'at-risk' and vulnerable subpopulations. Major disruptions to humanity such as the COVID-19 pandemic present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an under-recognition of mental ill-health as well as engagement difficulties which have hindered research, prevention and intervention efforts.

OBJECTIVE : To summarize and evaluate the literature on athletes' increased vulnerability to mental ill-health, and digital mental health solutions as a complement to prevention and intervention. To show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking and faster and better treatment algorithms.

METHODS : Mini review.

RESULTS : Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence as the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for utilization in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing and digital mental health screening and tracking tools have the potential to drive a consolidated, measurable and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health.

CONCLUSIONS : Athletes are an 'at-risk' subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience which helps them to positively adjust after a period of 'overwhelming' stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely utilized among general populations but there is yet to be an eminent athlete version. Sport in combination with mental health education should address the barriers to seeking help by increasing awareness of the range of mental ill-health through to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies that may be utilized in prevention and early intervention strategies.


Balcombe Luke, De Leo Diego


oncology Oncology


In Nucleic acids research ; h5-index 217.0

The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and

Frankish Adam, Diekhans Mark, Jungreis Irwin, Lagarde Julien, Loveland Jane E, Mudge Jonathan M, Sisu Cristina, Wright James C, Armstrong Joel, Barnes If, Berry Andrew, Bignell Alexandra, Boix Carles, Carbonell Sala Silvia, Cunningham Fiona, Di Domenico Tomás, Donaldson Sarah, Fiddes Ian T, García Girón Carlos, Gonzalez Jose Manuel, Grego Tiago, Hardy Matthew, Hourlier Thibaut, Howe Kevin L, Hunt Toby, Izuogu Osagie G, Johnson Rory, Martin Fergal J, Martínez Laura, Mohanan Shamika, Muir Paul, Navarro Fabio C P, Parker Anne, Pei Baikang, Pozo Fernando, Riera Ferriol Calvet, Ruffier Magali, Schmitt Bianca M, Stapleton Eloise, Suner Marie-Marthe, Sycheva Irina, Uszczynska-Ratajczak Barbara, Wolf Maxim Y, Xu Jinuri, Yang Yucheng T, Yates Andrew, Zerbino Daniel, Zhang Yan, Choudhary Jyoti S, Gerstein Mark, Guigó Roderic, Hubbard Tim J P, Kellis Manolis, Paten Benedict, Tress Michael L, Flicek Paul


Public Health Public Health

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

OBJECTIVE : This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

METHODS : Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

RESULTS : After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.

CONCLUSIONS : With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.


Chishtie Jawad Ahmed, Marchand Jean-Sebastien, Turcotte Luke A, Bielska Iwona Anna, Babineau Jessica, Cepoiu-Martin Monica, Irvine Michael, Munce Sarah, Abudiab Sally, Bjelica Marko, Hossain Saima, Imran Muhammad, Jeji Tara, Jaglal Susan


data mining, data visualization, health services research, machine learning, mobile phone, population health, visual analytics

Surgery Surgery

Applying the electronic nose for pre-operative SARS-CoV-2 screening.

In Surgical endoscopy ; h5-index 65.0

BACKGROUND : Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.

METHODS : Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.

RESULTS : 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.

CONCLUSIONS : The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Wintjens Anne G W E, Hintzen Kim F H, Engelen Sanne M E, Lubbers Tim, Savelkoul Paul H M, Wesseling Geertjan, van der Palen Job A M, Bouvy Nicole D


COVID-19, Electronic nose, Exhaled air, Innovative diagnostics, Volatile organic compounds

General General

Spread Mechanism and Influence Measurement of Online Rumors during COVID-19 Epidemic in China

ArXiv Preprint

In early 2020, the Corona Virus Disease 2019 (COVID-19) epidemic swept the world. In China, COVID-19 has caused severe consequences. Moreover, online rumors during COVID-19 epidemic increased people's panic about public health and social stability. Understanding and curbing the spread of online rumor is an urgent task at present. Therefore, we analyzed the rumor spread mechanism and proposed a method to quantify the rumor influence by the speed of new insiders. We use the search frequency of rumor as an observation variable of new insiders. We calculated the peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution. Then we designed several rumor features and used the above two coefficients as predictable labels. The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient. Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient. Meanwhile, features which are conducive to the outbreak are usually harmful to the continued spread of rumors. At the same time, anxiety is a crucial rumor-causing factor. Finally, we discussed how to use deep learning technology to reduce forecast loss by use BERT model.

Yiou Lin, Hang Lei, Yu Deng


General General

Review and Analysis of Massively Registered Clinical Trials of COVID-19 using the Text Mining Approach.

In Reviews on recent clinical trials

OBJECTIVE : Immediately after the outbreak of nCoV, many clinical trials are registered for COVID-19. The numbers of registrations are now raising inordinately. It is challenging to understand which research areas are explored in this massive pool of clinical studies. If such information can be compiled, then it is easy to explore new research studies for possible contributions in COVID-19 research.

METHODS : In the present work, a text-mining technique of artificial intelligence is utilized to map the research domains explored through the clinical trials of COVID-19. With the help of open-source and graphical user interface-based tool, 3007 clinical trials are analyzed here. The dataset is acquired from the international clinical trial registry platform of WHO. With the help of hierarchical cluster analysis, the clinical trials were grouped according to their common research studies. These clusters are analyzed manually using their word clouds for understanding the scientific area of a particular cluster. The scientific fields of clinical studies are comprehensively reviewed and discussed based on this analysis.

RESULTS : More than three-thousand clinical trial are grouped in 212 clusters by hierarchical cluster analysis. Manual intervention of these clusters using their individual word-cloud, helped to identify various scientific areas which are explored in COVID19 related clinical studies.

CONCLUSION : The text-mining is an easy and fastest way to explore many registered clinical trials. In our study, thirteen major clusters or research areas were identified in which the majority of clinical trials were registered. Many other uncategorized clinical studies were also identified as 'miscellaneousstudies'. The clinical trials within the individual cluster were studied, and their research purposes are compiled comprehensively in the present work.

Patel Swayamprakash, Patel Ashish, Patel Mruduka, Shah Umang, Patel Mehul, Solanki Nilay, Patel Suchita


COVID19, Natural Language Processing, Text-Mining, clinical trial, coronavirus, diagnosis, treatment.

General General

Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers Discovery

bioRxiv Preprint

Precision medicine, regarded as the future of healthcare, is gaining increasing attention these years. As an essential part of precision medicine, clinical omics have been successfully applied in disease diagnosis and prognosis using machine learning techniques. However, existing methods mainly make predictions based on gene-level individual features or their random combinations, none of the previous work has considered the activation of signaling pathways. Therefore, the model interpretability and accuracy are limited, and reasonable signaling pathways are yet to be discovered. In this paper, we propose a novel multi-level attention graph neural network (MLA-GNN), which applies weighted correlation network analysis (WGCNA) to format the omic data of each patient into graph-structured data, and then constructs multi-level graph features, and fuses them through a well-designed multi-level graph feature fully fusion (MGFFF) module to conduct multi-task prediction. Moreover, a novel full-gradient graph saliency mechanism is developed to make the MLA-GNN interpretable. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from COVID-19/non-COVID-19 patient sera. More importantly, the proposed model's decision can be interpreted in the signaling pathway level and is consistent with the clinical understanding.

Xing, X.; Yang, F.; Li, H.; Jiang, B.; Zhang, J.; Zhao, Y.; Huang, J.; Meng, M. Q.- H.; Yao, J.


Radiology Radiology

Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

ArXiv Preprint

Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical or semantic structure across varying images is a fundamental task in medical image analysis. In principle it is possible to use landmark detection or semantic segmentation for this task, but to work well these require large numbers of labeled data for each anatomical structure and sub-structure of interest. A more universal approach would discover the intrinsic structure from unlabeled images. We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM). SAM generates semantic embeddings for each image pixel that describes its anatomical location or body part. To produce such embeddings, we propose a pixel-level contrastive learning framework. A coarse-to-fine strategy ensures both global and local anatomical information are encoded. Negative sample selection strategies are designed to enhance the discriminability among different body parts. Using SAM, one can label any point of interest on a template image, and then locate the same body part in other images by simple nearest neighbor searching. We demonstrate the effectiveness of SAM in multiple tasks with 2D and 3D image modalities. On a chest CT dataset with 19 landmarks, SAM outperforms widely-used registration algorithms while being 200 times faster. On two X-ray datasets, SAM, with only one labeled template image, outperforms supervised methods trained on 50 labeled images. We also apply SAM on whole-body follow-up lesion matching in CT and obtain an accuracy of 91%.

Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Adam P. Harrison, Dazhou Guo, Youbao Tang, Jing Xiao, Jingjing Lu, Le Lu


General General

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset.

In SN computer science

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

Muhammad L J, Algehyne Ebrahem A, Usman Sani Sharif, Ahmad Abdulkadir, Chakraborty Chinmay, Mohammed I A


COVID-19, Dataset, Decision tree, Machine learning, Pandemic

Surgery Surgery

Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

In Scandinavian journal of trauma, resuscitation and emergency medicine ; h5-index 32.0

BACKGROUND : Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments.

METHODS : This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol.

RESULTS : Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.

CONCLUSION : Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Langer Thomas, Favarato Martina, Giudici Riccardo, Bassi Gabriele, Garberi Roberta, Villa Fabiana, Gay Hedwige, Zeduri Anna, Bragagnolo Sara, Molteni Alberto, Beretta Andrea, Corradin Matteo, Moreno Mauro, Vismara Chiara, Perno Carlo Federico, Buscema Massimo, Grossi Enzo, Fumagalli Roberto


Artificial intelligence, Critical care, Emergency service, hospital, Pandemics, Severe acute respiratory syndrome coronavirus 2, Supervised machine learning

Public Health Public Health

Anxiety in neurosurgical patients undergoing nonurgent surgery during the COVID-19 pandemic.

In Neurosurgical focus ; h5-index 45.0

OBJECTIVE : The COVID-19 pandemic has forced many countries into lockdown and has led to the postponement of nonurgent neurosurgical procedures. Although stress has been investigated during this pandemic, there are no reports on anxiety in neurosurgical patients undergoing nonurgent surgical procedures.

METHODS : Neurosurgical patients admitted to hospitals in eastern Lombardy for nonurgent surgery after the lockdown prospectively completed a pre- and postoperative structured questionnaire. Recorded data included demographics, pathology, time on surgical waiting list, anxiety related to COVID-19, primary pathology and surgery, safety perception during hospital admission before and after surgery, and surgical outcomes. Anxiety was measured with the State-Trait Anxiety Inventory. Descriptive statistics were computed on the different variables and data were stratified according to pathology (oncological vs nononcological). Three different models were used to investigate which variables had the greatest impact on anxiety, oncological patients, and safety perception, respectively. Because the variables (Xs) were of a different nature (qualitative and quantitative), mostly asymmetrical, and related to outcome (Y) by nonlinear relationships, a machine learning approach composed of three steps (1, random forest growing; 2, relative variable importance measure; and 3, partial dependence plots) was chosen.

RESULTS : One hundred twenty-three patients from 10 different hospitals were included in the study. None of the patients developed COVID-19 after surgery. State and trait anxiety were reported by 30.3% and 18.9% of patients, respectively. Higher values of state anxiety were documented in oncological compared to nononcological patients (46.7% vs 25%; p = 0.055). Anxiety was strongly associated with worry about primary pathology, surgery, disease worsening, and with stress during waiting time, as expected. Worry about positivity to SARS-CoV-2, however, was the strongest factor associated with anxiety, even though none of the patients were infected. Neuro-oncological disease was associated with state anxiety and with worry about surgery and COVID-19. Increased bed distance and availability of hand sanitizer were associated with a feeling of safety.

CONCLUSIONS : These data underline the importance of psychological support, especially for neuro-oncological patients, during a pandemic.

Doglietto Francesco, Vezzoli Marika, Biroli Antonio, Saraceno Giorgio, Zanin Luca, Pertichetti Marta, Calza Stefano, Agosti Edoardo, Aliaga Arias Jahard Mijail, Assietti Roberto, Bellocchi Silvio, Bernucci Claudio, Bistazzoni Simona, Bongetta Daniele, Fanti Andrea, Fioravanti Antonio, Fiorindi Alessandro, Franzin Alberto, Locatelli Davide, Pugliese Raffaelino, Roca Elena, Sicuri Giovanni Marco, Stefini Roberto, Venturini Martina, Vivaldi Oscar, Zattra Costanza, Zoia Cesare, Fontanella Marco Maria


anxiety, machine learning, pandemic

Radiology Radiology

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.

In Investigative radiology ; h5-index 46.0

** : The aim of this study was to compare a diagnosis support system to detect COVID-19 pneumonia on chest radiographs (CXRs) against radiologists of various levels of expertise in chest imaging.

MATERIALS AND METHODS : Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system.

RESULTS : The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001).

CONCLUSIONS : The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.

Fontanellaz Matthias, Ebner Lukas, Huber Adrian, Peters Alan, Löbelenz Laura, Hourscht Cynthia, Klaus Jeremias, Munz Jaro, Ruder Thomas, Drakopoulos Dionysios, Sieron Dominik, Primetis Elias, Heverhagen Johannes T, Mougiakakou Stavroula, Christe Andreas


Public Health Public Health

Reusable Self-Sterilization Masks Based on Electrothermal Graphene Filters.

In ACS applied materials & interfaces ; h5-index 147.0

Surgical mask is recommended by the World Health Organization for personal protection against disease transmission. However, most of the surgical masks on the market are disposable that cannot be self-sterilized for reuse. Thus, when confronting the global public health crisis, a severe shortage of mask resource is inevitable. In this paper, a novel low-cost electrothermal mask with excellent self-sterilization performance and portability is reported to overcome this shortage. First, a flexible, ventilated, and conductive cloth tape is patterned and adhered to the surface of a filter layer made of melt-blown nonwoven fabrics (MNF), which functions as interdigital electrodes. Then, a graphene layer with premier electric and thermal conductivity is coated onto the MNF. Operating under a low voltage of 3 V, the graphene-modified MNF (mod-MNF) can quickly generate large amounts of heat to achieve a high temperature above 80 °C, which can kill the majority of known viruses attached to the filter layer and the mask surface. Finally, the optimized graphene-modified masks based on the mod-MNF filter retain a relatively high particulate matter (PM) removal efficiency and a low-pressure drop. Moreover, the electrothermal masks can maintain almost the same PM removal efficiency over 10 times of electrifying, suggesting its outstanding reusability.

Shan Xiaoli, Zhang Han, Liu Cihui, Yu Liyan, Di Yunsong, Zhang Xiaowei, Dong Lifeng, Gan Zhixing


COVID-19, electrothermal effect, graphene ink, self-sterilization, surgical masks

Surgery Surgery

Lung transplantation for patients with severe COVID-19.

In Science translational medicine ; h5-index 138.0

Lung transplantation can potentially be a life-saving treatment for patients with non-resolving COVID-19-associated respiratory failure. Concerns limiting lung transplantation include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and the potential risk for allograft infection by pathogens causing ventilator-associated pneumonia in the native lung. Importantly, the native lung might recover, resulting in long-term outcomes preferable to those of transplant. Here, we report the results of lung transplantation in three patients with non-resolving COVID-19-associated respiratory failure. We performed single molecule fluorescent in situ hybridization (smFISH) to detect both positive and negative strands of SARS-CoV-2 RNA in explanted lung tissue from the three patients and in additional control lung tissue samples. We conducted extracellular matrix imaging and single cell RNA sequencing on explanted lung tissue from the three patients who underwent transplantation and on warm post-mortem lung biopsies from two patients who had died from COVID-19-associated pneumonia. Lungs from these five patients with prolonged COVID-19 disease were free of SARS-CoV-2 as detected by smFISH, but pathology showed extensive evidence of injury and fibrosis that resembled end-stage pulmonary fibrosis. Using machine learning, we compared single cell RNA sequencing data from the lungs of patients with late stage COVID-19 to that from the lungs of patients with pulmonary fibrosis and identified similarities in gene expression across cell lineages. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is their only option for survival.

Bharat Ankit, Querrey Melissa, Markov Nikolay S, Kim Samuel, Kurihara Chitaru, Garza-Castillon Rafael, Manerikar Adwaiy, Shilatifard Ali, Tomic Rade, Politanska Yuliya, Abdala-Valencia Hiam, Yeldandi Anjana V, Lomasney Jon W, Misharin Alexander V, Budinger G R Scott


Public Health Public Health

Prediction of the incubation period for COVID-19 and future virus disease outbreaks.

In BMC biology

BACKGROUND : A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood.

RESULTS : We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks.

CONCLUSIONS : The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.

Gussow Ayal B, Auslander Noam, Wolf Yuri I, Koonin Eugene V


COVID-19, Coronavirus, Incubation period, Machine learning, Pandemic, Respiratory disease, Respiratory infections, SARS-CoV-2

General General

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine.

In International journal of environmental research and public health ; h5-index 73.0

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

Franchini Michela, Pieroni Stefania, Martini Nicola, Ripoli Andrea, Chiappino Dante, Denoth Francesca, Liebman Michael Norman, Molinaro Sabrina, Della Latta Daniele


COVID-19, SARS-CoV-2, co-morbidity profile, participatory medicine, telegram bot

General General

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.

In Journal of clinical medicine

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11-0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

Burdick Hoyt, Lam Carson, Mataraso Samson, Siefkas Anna, Braden Gregory, Dellinger R Phillip, McCoy Andrea, Vincent Jean-Louis, Green-Saxena Abigail, Barnes Gina, Hoffman Jana, Calvert Jacob, Pellegrini Emily, Das Ritankar


COVID-19, SARS-Cov-2, drug treatment, hydroxychloroquine, machine learning, mortality, prediction