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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

Radiology Radiology

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19.

OBJECTIVE : We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection.

METHODS : In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia.

RESULTS : Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%).

CONCLUSIONS : Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Xu Ming, Ouyang Liu, Han Lei, Sun Kai, Yu Tingting, Li Qian, Tian Hua, Safarnejad Lida, Zhang Hengdong, Gao Yue, Bao Forrest Sheng, Chen Yuanfang, Robinson Patrick, Ge Yaorong, Zhu Baoli, Liu Jie, Chen Shi


COVID-19, biomedical imaging, deep learning, diagnosis, diagnosis support, diagnostic, differentiation, feature fusion, imaging, machine learning, multimodal, testing

General General

COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.

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

Today's societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March-April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government's 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.

Alomari Ebtesam, Katib Iyad, Albeshri Aiiad, Mehmood Rashid


Arabic language, COVID-19, Triple Bottom Line (TBL), Twitter, apache spark, big data, coronavirus, distributed computing, machine learning, smart cities, smart governance, smart healthcare, social media

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

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

In Interdisciplinary sciences, computational life sciences

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.

Rasheed Jawad, Hameed Alaa Ali, Djeddi Chawki, Jamil Akhtar, Al-Turjman Fadi


Artificial neural network, COVID-19, Computer-aided diagnosis, Image classification, Principal component analysis

Public Health Public Health

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In Human genomics

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Laponogov Ivan, Gonzalez Guadalupe, Shepherd Madelen, Qureshi Ahad, Veselkov Dennis, Charkoftaki Georgia, Vasiliou Vasilis, Youssef Jozef, Mirnezami Reza, Bronstein Michael, Veselkov Kirill


Antiviral, COVID-19, Drug repositioning, Food, Gene-gene networks, Interactomics, Machine learning, SARS-CoV-2

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