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General General

The Reference Model: An Initial Use Case for COVID-19.

In Cureus

The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques.  The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors.

Barhak Jacob


disease modeling, estimation, high performance computing, machine learning, monte-carlo, optimization, population modeling

Internal Medicine Internal Medicine

Understanding Public Perception of COVID-19 Social Distancing on Twitter.

In Infection control and hospital epidemiology ; h5-index 48.0

OBJECTIVE : Social distancing policies are key in curtailing COVID-19 infection spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We sought to study public perception of social distancing through organic, large-scale discussion on Twitter.

DESIGN : Retrospective cross-sectional study.

METHODS : Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine learning models, conducting a sentiment analysis to identify emotions and polarity. We evaluated subjectivity of tweets and estimated frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments.

RESULTS : We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half (50.4%) of tweets primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (i.e., joy), concerns about food insecurity and quarantine effects (i.e., fear), and unpredictability of COVID and its implications (i.e., surprise).

CONCLUSIONS : The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing in the early stages of their implementation.

Saleh Sameh N, Lehmann Christoph U, McDonald Samuel A, Basit Mujeeb A, Medford Richard J


Radiology Radiology

Imaging of COVID-19 pneumonia: Patterns, pathogenesis, and advances.

In The British journal of radiology

COVID-19 pneumonia is a newly recognized lung infection. Initially, CT imaging was demonstrated to be one of the most sensitive tests for the detection of infection. Currently, with broader availability of polymerase chain reaction for disease diagnosis, CT is mainly used for the identification of complications and other defined clinical indications in hospitalized patients. Nonetheless, radiologists are interpreting lung imaging in unsuspected patients as well as in suspected patients with imaging obtained to rule out other relevant clinical indications. The knowledge of pathological findings is also crucial for imagers to better interpret various imaging findings. Identification of the imaging findings that are commonly seen with the disease is important to diagnose and suggest confirmatory testing in unsuspected cases. Proper precautionary measures will be important in such unsuspected patients to prevent further spread. In addition to understanding the imaging findings for the diagnosis of the disease, it is important to understand the growing set of tools provided by artificial intelligence. The goal of this review is to highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19. We briefly address the known pathological findings of the COVID-19 lung disease that may help better understand the imaging appearance, and we provide a demonstration of novel display methodologies and artificial intelligence applications serving to support clinical observations.

Nagpal Prashant, Narayanasamy Sabarish, Vidholia Aditi, Guo Junfeng, Shin Kyung Min, Lee Chang Hyun, Hoffman Eric A


Radiology Radiology

CT Manifestations of Coronavirus Disease (COVID-19) Pneumonia and Influenza Virus Pneumonia: A Comparative Study.

In AJR. American journal of roentgenology

To listen to the podcast associated with this article, please select one of the following: iTunes, Google Play, or direct download. OBJECTIVE. The purpose of this study was to investigate differences in CT manifestations of coronavirus disease (COVID-19) pneumonia and those of influenza virus pneumonia. MATERIALS AND METHODS. We conducted a retrospective study of 52 patients with COVID-19 pneumonia and 45 patients with influenza virus pneumonia. All patients had positive results for the respective viruses from nucleic acid testing and had complete clinical data and CT images. CT findings of pulmonary inflammation, CT score, and length of largest lesion were evaluated in all patients. Mean density, volume, and mass of lesions were further calculated using artificial intelligence software. CT findings and clinical data were evaluated. RESULTS. Between the group of patients with COVID-19 pneumonia and the group of patients with influenza virus pneumonia, the largest lesion close to the pleura (i.e., no pulmonary parenchyma between the lesion and the pleura), mucoid impaction, presence of pleural effusion, and axial distribution showed statistical difference (p < 0.05). The properties of the largest lesion, presence of ground-glass opacity, presence of consolidation, mosaic attenuation, bronchial wall thickening, centrilobular nodules, interlobular septal thickening, crazy paving pattern, air bronchogram, unilateral or bilateral distribution, and longitudinal distribution did not show significant differences (p > 0.05). In addition, no significant difference was seen in CT score, length of the largest lesion, mean density, volume, or mass of the lesions between the two groups (p > 0.05). CONCLUSION. Most lesions in patients with COVID-19 pneumonia were located in the peripheral zone and close to the pleura, whereas influenza virus pneumonia was more prone to show mucoid impaction and pleural effusion. However, differentiating between COVID-19 pneumonia and influenza virus pneumonia in clinical practice remains difficult.

Lin Liaoyi, Fu Gangze, Chen Shuangli, Tao Jiejie, Qian Andan, Yang Yunjun, Wang Meihao


COVID-19, CT, coronavirus disease, imaging features, infection, influenza virus, novel coronavirus, pneumonia

General General

Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.

In IEEE pervasive computing

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

Rehm Gregory B, Woo Sang Hoon, Chen Xin Luigi, Kuhn Brooks T, Cortes-Puch Irene, Anderson Nicholas R, Adams Jason Y, Chuah Chen-Nee

General General

Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

The whole world is facing a great challenging time due to Coronavirus disease (COVID-19) caused by SARS-CoV-2. Globally, more than 14.6 M people have been diagnosed and more than 595 K deaths are reported. Currently, no effective vaccine or drugs are available to combat COVID-19. Therefore, the whole world is looking for new drug candidates that can treat the COVID-19. In this study, we conducted a virtual screening of natural compounds using a deep-learning method. A deep-learning algorithm was used for the predictive modeling of a CHEMBL3927 dataset of inhibitors of Main protease (Mpro). Several predictive models were developed and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model with R2=0.83, MAE = 1.06, MSE = 1.5, RMSE = 1.2, and loss = 1.5 was deployed on the Selleck database containing 1611 natural compounds for virtual screening. The model predicted 500 hits showing the value score between 6.9 and 3.8. The screened compounds were further enriched by molecular docking resulting in 39 compounds based on comparison with the reference (X77). Out of them, only four compounds were found to be drug-like and three were non-toxic. The complexes of compounds and Mpro were finally subjected to Molecular dynamic (MD) simulation for 100 ns. The MMPBSA result showed that two compounds Palmatine and Sauchinone formed very stable complex with Mpro and had free energy of -71.47 kJ mol-1 and -71.68 kJ mol-1 respectively as compared to X77 (-69.58 kJ mol-1). From this study, we can suggest that the identified natural compounds may be considered for therapeutic development against the SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Joshi Tanuja, Joshi Tushar, Pundir Hemlata, Sharma Priyanka, Mathpal Shalini, Chandra Subhash


COVID-19, deep learning, main protease (MPro), molecular docking, natural compounds

General General

Face Masks and Respirators in the Fight against the COVID-19 Pandemic: A Review of Current Materials, Advances and Future Perspectives.

In Materials (Basel, Switzerland)

The outbreak of COVID-19 has spread rapidly across the globe, greatly affecting how humans as a whole interact, work and go about their daily life. One of the key pieces of personal protective equipment (PPE) that is being utilised to return to the norm is the face mask or respirator. In this review we aim to examine face masks and respirators, looking at the current materials in use and possible future innovations that will enhance their protection against SARS-CoV-2. Previous studies concluded that cotton, natural silk and chiffon could provide above 50% efficiency. In addition, it was found that cotton quilt with a highly tangled fibrous nature provides efficient filtration in the small particle size range. Novel designs by employing various filter materials such as nanofibres, silver nanoparticles, and nano-webs on the filter surfaces to induce antimicrobial properties are also discussed in detail. Modification of N95/N99 masks to provide additional filtration of air and to deactivate the pathogens using various technologies such as low- temperature plasma is reviewed. Legislative guidelines for selecting and wearing facial protection are also discussed. The feasibility of reusing these masks will be examined as well as a discussion on the modelling of mask use and the impact wearing them can have. The use of Artificial Intelligence (AI) models and its applications to minimise or prevent the spread of the virus using face masks and respirators is also addressed. It is concluded that a significant amount of research is required for the development of highly efficient, reusable, anti-viral and thermally regulated face masks and respirators.

O’Dowd Kris, Nair Keerthi M, Forouzandeh Parnia, Mathew Snehamol, Grant Jamie, Moran Ruth, Bartlett John, Bird Jerry, Pillai Suresh C


SARS-CoV-2, droplets, facemasks, legislations, modelling, personal protective equipment (PPE), respirators, reuse, testing

General General

Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models.

In IEEE journal of biomedical and health informatics

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.

Bridge Joshua, Meng Yanda, Zhao Yitian, Du Yong, Zhao Mingfeng, Sun Renrong, Zheng Yalin


General General

α-Satellite: An AI-driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

In IEEE journal of biomedical and health informatics

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicates the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website1.

Ye Yanfang, Hou Shifu, Fan Yujie, Zhang Yiming, Qian Yiyue, Sun Shiyu, Peng Qian, Ju Mingxuan, Song Wei, Loparo Kenneth


General General

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.

In IEEE journal of biomedical and health informatics

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Jelodar Hamed, Wang Yongli, Orji Rita, Huang Hucheng


General General

Closing the COVID-19 Psychological Treatment Gap for Cancer Patients in Alberta: Protocol for Implementation and Evaluation of Text4Hope-Cancer Care.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Background: Cancer diagnoses and treatments usually engender significant anxiety and depressive symptoms in patients, close relatives, and caregivers. During the COVID-19 pandemic providing psychological support in this context presents additional challenges due to self-isolation and social or physical distancing measures in place to limit viral spread. This protocol describes the use of text messaging (Text4Hope-Cancer Care) as a convenient, cost-effective, and accessible population-level mental health intervention. This program is evidence-based with prior research supporting good outcomes and high user satisfaction.

OBJECTIVE : We will implement daily supportive text messaging (Text4Hope-Cancer Care) as a way of reducing/managing anxiety and depression related to cancer diagnosis and treatment in Alberta. Prevalence of anxiety and depressive symptoms, their demographic correlates, and Text4Hope-Cancer Care induced changes in anxiety and depression will be evaluated.

METHODS : Alberta residents with a cancer diagnosis and loved ones of those dealing with cancer diagnosis can self-subscribe to the Text4Hope-Cancer Care program by texting "CancerCare" to a dedicated text number. Self-administered, anonymous, online questionnaires will be used to assess anxiety and depressive symptoms using the Hospital Anxiety and Depression Scale (HADS). Data will be collected at onset of individuals receiving text messages, and at the mid and endpoints of the program (i.e., 6- and 12-weeks).

RESULTS : Data will be analyzed with parametric and non-parametric statistics for primary outcomes (i.e., anxiety and depressive symptoms) and metrics of use, including number of subscribers and user satisfaction. In addition, data-mining and machine learning analysis will focus on determining characteristics of subscribers that predict high levels of symptoms of mental disorders, and may subsequently predict changes in those measures in response to the Text4Hope-Cancer Care program.

CONCLUSIONS : Text4Hope-Cancer Care has the potential to provide key information regarding prevalence rates of anxiety and depressive symptoms in patients diagnosed or receiving care for cancer and their caregivers. The study will generate demographic correlates of anxiety and depression, and outcome data related to this scalable, population-level intervention. Information from this study will be valuable for healthcare practitioners working in cancer care and may help inform policy and decision-making regarding psychological interventions for cancer care.

CLINICALTRIAL : Ethics approval has been granted by the University of Alberta Health Research Ethics Board (Pro00086163).

Agyapong Vincent Israel Opoku, Hrabok Marianne, Shalaby Reham, Mrklas Kelly, Vuong Wesley, Gusnowski April, Surood Shireen, Greenshaw Andrew James, Nkire Nnamdi


Public Health Public Health

Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: An Infoveillance Study on Twitter and Instagram.

In JMIR public health and surveillance

BACKGROUND : The COVID-19 pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel "infodemic", including the online marketing and sale of unapproved, illegal and counterfeit COVID-19 health products, including testing kits, treatments, and other questionable "cures". Enabling proliferation of this content is growing ubiquity of Internet-based technologies, including popular social media platforms that now have billions of global users.

OBJECTIVE : To collect, analyze, identify and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related healthcare products from Twitter and Instagram.

METHODS : The study was conducted in two phases beginning with collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter API for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence.

RESULTS : We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March - April for Twitter and February - May for Instagram. After applying our NLP and deep learning approaches, we identified 1,271 tweets and 596 Instagram posts associated with questionable sales of COVID-19-related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods.

CONCLUSIONS : Results from this study provide initial insight into one front of the "infodemic" fight against COVID-19 by characterizing what types of health products, selling claims and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

CLINICALTRIAL : Not applicable.

Mackey Tim, Li Jiawei, Purushothaman Vidya, Nali Matthew, Shah Neal, Bardier Cortni, Cai Mingxiang, Liang Bryan


General General

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments and individuals to navigate the pandemic.

OBJECTIVE : To analyze social network discussion on Twitter related to COVID-19 and to investigate the sentiments towards COVID-19.

METHODS : This study applied machine learning methods in the field of artificial intelligence to analyze data collected from the Twitter website. Using tweets originating exclusively in the United States and written in English during the one-month period from March 20, 2020 to April 19, 2020, the study examined COVID-19 related discussions. Social network and sentiment analyses were also conducted to determine whether the tweets expressed positive, neutral or negative sentiments and the social network of dominant topics, as well as geographic analysis of the tweets.

RESULTS : There was a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets and 641,381 mentions in tweets during the study timeframe. Sentiment analysis classified 434,254 (48.2%) tweets as positive, 187,042 (20.7%) as neutral and 280,842 (31.1%) as having negative COVID-19 sentiment. The study identified five dominant themes among COVID-19 related tweets: healthcare environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee and North Carolina conveyed the most positive sentiment.

CONCLUSIONS : This study identified five prevalent themes of COVID-19 discussion with sentiments ranging from positive, negative, and neutral. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic.

Hung Man, Lauren Evelyn, Hon Eric S, Birmingham Wendy C, Xu Julie, Su Sharon, Hon Shirley D, Park Jungweon, Dang Peter, Lipsky Martin S


General General

Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Early Containment using Machine Learning.

In Transboundary and emerging diseases ; h5-index 40.0

COVID-19 pandemic disease spread by the SARS-COV-2 single-strand structure RNA virus, belongs to the 7th generation of the coronavirus family. Following an unusual replication mechanism, it's extreme ease of transmissivity has put many counties under lockdown. With uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of the COVID-19 virus and identifies countries that have shown early signs of containment until 26th March 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection-related independent variables to predict early containment. COVID-19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% to 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.

Kasilingam Dharun, Prabhakaran S P Sathiya, Dinesh Kumar R, Rajagopal Varthini, Santhosh Kumar T, Soundararaj Ajitha


COVID-19, Corona Virus, Exponential Growth Model, Machine Learning, Pandemic, SARS-CoV-2

General General

Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic.

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

Coronavirus Disease 2019 (COVID-19) has affected day to day life and slowed down the global economy. Most countries are enforcing strict quarantine to control the havoc of this highly contagious disease. Since the outbreak of COVID-19, many data analyses have been done to provide close support to decision-makers. We propose a method comprising data analytics and machine learning classification for evaluating the effectiveness of lockdown regulations. Lockdown regulations should be reviewed on a regular basis by governments, to enable reasonable control over the outbreak. The model aims to measure the efficiency of lockdown procedures for various countries. The model shows a direct correlation between lockdown procedures and the infection rate. Lockdown efficiency is measured by finding a correlation coefficient between lockdown attributes and the infection rate. The lockdown attributes include retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, residential, and schools. Our results show that combining all the independent attributes in our study resulted in a higher correlation (0.68) to the dependent value Interquartile 3 (Q3). Mean Absolute Error (MAE) was found to be the least value when combining all attributes.

Al Zobbi Mohammed, Alsinglawi Belal, Mubin Omar, Alnajjar Fady


COVID-19, basic reproduction number, government regulations, infectious disease modeling, machine learning, spread control

General General

A deep learning framework for high-throughput mechanism-driven phenotype compound screening.

In bioRxiv : the preprint server for biology

Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Pham Thai-Hoang, Qiu Yue, Zeng Jucheng, Xie Lei, Zhang Ping


General General

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays.

In IEEE access : practical innovations, open solutions

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

Rajaraman Sivaramakrishnan, Siegelman Jen, Alderson Philip O, Folio Lucas S, Folio Les R, Antani Sameer K


COVID-19, Convolutional neural network, Deep learning, Ensemble, Iterative pruning

Radiology Radiology

Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays.

In Experimental and therapeutic medicine

COVID-19 has led to an unprecedented healthcare crisis with millions of infected people across the globe often pushing infrastructures, healthcare workers and entire economies beyond their limits. The scarcity of testing kits, even in developed countries, has led to extensive research efforts towards alternative solutions with high sensitivity. Chest radiological imaging paired with artificial intelligence (AI) can offer significant advantages in diagnosis of novel coronavirus infected patients. To this end, transfer learning techniques are used for overcoming the limitations emanating from the lack of relevant big datasets, enabling specialized models to converge on limited data, as in the case of X-rays of COVID-19 patients. In this study, we present an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions. The proposed transfer learning methodology achieves an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.

Tsiknakis Nikos, Trivizakis Eleftherios, Vassalou Evangelia E, Papadakis Georgios Z, Spandidos Demetrios A, Tsatsakis Aristidis, Sánchez-García Jose, López-González Rafael, Papanikolaou Nikolaos, Karantanas Apostolos H, Marias Kostas


COVID-19, chest X-rays, interpretable artificial intelligence, transfer learning

Public Health Public Health

Roadmap to strengthen global mental health systems to tackle the impact of the COVID-19 pandemic.

In International journal of mental health systems

Background : The COVID pandemic has been devastating for not only its direct impact on lives, physical health, socio-economic status of individuals, but also for its impact on mental health. Some individuals are affected psychologically more severely and will need additional care. However, the current health system is so fragmented and focused on caring for those infected that management of mental illness has been neglected. An integrated approach is needed to strengthen the health system, service providers and research to not only manage the current mental health problems related to COVID but develop robust strategies to overcome more long-term impact of the pandemic. A series of recommendations are outlined in this paper to help policy makers, service providers and other stakeholders, and research and research funders to strengthen existing mental health systems, develop new ones, and at the same time advance research to mitigate the mental health impact of COVID19. The recommendations refer to low, middle and high resource settings as capabilities vary greatly between countries and within countries.

Discussion : The recommendations for policy makers are focused on strengthening leadership and governance, finance mechanisms, and developing programme and policies that especially include the most vulnerable populations. Service provision should focus on accessible and equitable evidence-based community care models commensurate with the existing mental health capacity to deliver care, train existing primary care staff to cater to increased mental health needs, implement prevention and promotion programmes tailored to local needs, and support civil societies and employers to address the increased burden of mental illness. Researchers and research funders should focus on research to develop robust information systems that can be enhanced further by linking with other data sources to run predictive models using artificial intelligence, understand neurobiological mechanisms and community-based interventions to address the pandemic driven mental health problems in an integrated manner and use innovative digital solutions.

Conclusion : Urgent action is needed to strengthen mental health system in all settings. The recommendations outlined can be used as a guide to develop these further or identify new ones in relation to local needs.

Maulik Pallab K, Thornicroft Graham, Saxena Shekhar


COVID 19, Mental health resources, Mental health services, Mental health systems, Policy making, Research

General General

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

ArXiv Preprint

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras


Cardiology Cardiology

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.

In Journal of medical systems ; h5-index 48.0

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

Bansal Agam, Padappayil Rana Prathap, Garg Chandan, Singal Anjali, Gupta Mohak, Klein Allan


Artificial intelligence, COVID 19, Machine learning, Outcome prediction

General General

Network perturbation analysis in human bronchial epithelial cells following SARS-CoV2 infection.

In Experimental cell research

BACKGROUND : SARS-CoV2, the agent responsible for the current pandemic, is also causing respiratory distress syndrome (RDS), hyperinflammation and high mortality. It is critical to dissect the pathogenetic mechanisms in order to reach a targeted therapeutic approach.

METHODS : In the present investigation, we evaluated the effects of SARS-CoV2 on human bronchial epithelial cells (HBEC). We used RNA-seq datasets available online for identifying SARS-CoV2 potential genes target on human bronchial epithelial cells. RNA expression levels and potential cellular gene pathways have been analysed. In order to identify possible common strategies among the main pandemic viruses, such as SARS-CoV2, SARS-CoV1, MERS-CoV, and H1N1, we carried out a hypergeometric test of the main genes transcribed in the cells of the respiratory tract exposed to these viruses.

RESULTS : The analysis showed that two mechanisms are highly regulated in HBEC: the innate immunity recruitment and the disassembly of cilia and cytoskeletal structure. The granulocyte colony-stimulating factor (CSF3) and dynein heavy chain 7, axonemal (DNAH7) represented respectively the most upregulated and downregulated genes belonging to the two mechanisms highlighted above. Furthermore, the carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7) that codifies for a surface protein is highly specific of SARS-CoV2 and not for SARS-CoV1, MERS-CoV, and H1N1, suggesting a potential role in viral entry. In order to identify potential new drugs, using a machine learning approach, we highlighted Flunisolide, Thalidomide, Lenalidomide, Desoximetasone, xylazine, and salmeterol as potential drugs against SARS-CoV2 infection.

CONCLUSIONS : Overall, lung involvement and RDS could be generated by the activation and down regulation of diverse gene pathway involving respiratory cilia and muscle contraction, apoptotic phenomena, matrix destructuration, collagen deposition, neutrophil and macrophages recruitment.

Nunnari Giuseppe, Sanfilippo Cristina, Castrogiovanni Paola, Imbesi Rosa, Volti Giovanni Li, Barbagallo Ignazio, Musumeci Giuseppe, Di Rosa Michelino


Bioinformatics, CEACAM7, COVID-19, CSF3, DNAH7, Innate immunity, Respiratory cilia, SARS-CoV(2), c8orf4

Public Health Public Health

Prognostic modelling of COVID-19 using artificial intelligence in a UK population.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The current severe acute respiratory syndrome-coronavirus disease (SARS-CoV-2) outbreak is a public health emergency which has had a significant case-fatality in the United Kingdom (UK). Whilst there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to SARS-CoV-2 positive patients.

OBJECTIVE : To create a point-of-admission, mortality-risk scoring system utilising an artificial neural network (ANN).

METHODS : We present an ANN which can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyses a set of patient features including demographics, comorbidities, smoking history and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcriptase polymerase chain reaction (rt-PCR) test for SARS-CoV-2.

RESULTS : Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI: 61.65% to 98.45%) and specificity of 85.94% (95% CI: 74.98% to 93.36%). The positive predictive value was 60.87% (95% CI: 45.23% to 74.56%), and the negative predictive value was 96.49% (95% CI: 88.23% to 99.02%). The (AUROC) was 90.12%.

CONCLUSIONS : This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.


Abdulaal Ahmed, Patel Aatish, Charani Esmita, Denny Sarah, Mughal Nabeela, Moore Luke


General General

Detection of SARS-CoV-2 in nasal swabs using MALDI-MS.

In Nature biotechnology ; h5-index 151.0

Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.

Nachtigall Fabiane M, Pereira Alfredo, Trofymchuk Oleksandra S, Santos Leonardo S


Public Health Public Health

Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The inherent difficulty to identify and monitor emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing COVID-19 outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events.

OBJECTIVE : Our aim is to propose a methodology able to forecast COVID-19 in real-time.

METHODS : We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method uses as inputs (a) official health reports (b) COVID-19-related internet search activity (c) news media activity and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number 1 of historical disease observations, characteristic of emerging outbreaks.

RESULTS : Our model is able to produce stable and accurate forecasts two days ahead of current time, and outperforms a collection of baseline models in 27 out of the 32 Chinese provinces.

CONCLUSIONS : Our methodology could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.


Poirier Canelle, Liu Dianbo, Clemente Leonardo, Ding Xiyu, Chinazzi Matteo, Davis Jessica, Vespignani Alessandro, Santillana Mauricio


General General

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.

In IEEE transactions on medical imaging ; h5-index 74.0

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.

Wang Guotai, Liu Xinglong, Li Chaoping, Xu Zhiyong, Ruan Jiugen, Zhu Haifeng, Meng Tao, Li Kang, Huang Ning, Zhang Shaoting


General General

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.

In IEEE transactions on medical imaging ; h5-index 74.0

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

Zhou Longxi, Li Zhongxiao, Zhou Juexiao, Li Haoyang, Chen Yupeng, Huang Yuxin, Xie Dexuan, Zhao Lintao, Fan Ming, Hashmi Shahrukh, Abdelkareem Faisal, Eiada Riham, Xiao Xigang, Li Lihua, Qiu Zhaowen, Gao Xin


Radiology Radiology

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.

In Korean journal of radiology

OBJECTIVE : To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.

MATERIALS AND METHODS : In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated.

RESULTS : Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001).

CONCLUSION : Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

Hwang Eui Jin, Kim Hyungjin, Yoon Soon Ho, Goo Jin Mo, Park Chang Min


COVID-19, COVID-19 diagnostic testing, Deep learning, Pneumonia, Radiography, thoracic

Radiology Radiology

Effects of Weather on Coronavirus Pandemic.

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

The novel coronavirus (SARS-CoV-2) has spread globally and has been declared a pandemic by the World Health Organization. While influenza virus shows seasonality, it is unknown if COVID-19 has any weather-related affect. In this work, we analyze the patterns in local weather of all the regions affected by COVID-19 globally. Our results indicate that approximately 85% of the COVID-19 reported cases until 1 May 2020, making approximately 3 million reported cases (out of approximately 29 million tests performed) have occurred in regions with temperature between 3 and 17 °C and absolute humidity between 1 and 9 g/m3. Similarly, hot and humid regions outside these ranges have only reported around 15% or approximately 0.5 million cases (out of approximately 7 million tests performed). This suggests that weather might be playing a role in COVID-19 spread across the world. However, this role could be limited in US and European cities (above 45 N), as mean temperature and absolute humidity levels do not reach these ranges even during the peak summer months. For hot and humid countries, most of them have already been experiencing temperatures >35 °C and absolute humidity >9 g/m3 since the beginning of March, and therefore the effect of weather, however little it is, has already been accounted for in the COVID-19 spread in those regions, and they must take strict social distancing measures to stop the further spread of COVID-19. Our analysis showed that the effect of weather may have only resulted in comparatively slower spread of COVID-19, but not halted it. We found that cases in warm and humid countries have consistently increased, accounting for approximately 500,000 cases in regions with absolute humidity >9 g/m3, therefore effective public health interventions must be implemented to stop the spread of COVID-19. This also means that 'summer' would not alone stop the spread of COVID-19 in any part of the world.

Bukhari Qasim, Massaro Joseph M, D’Agostino Ralph B, Khan Sheraz


COVID, COVID-19, coronavirus, humidity, temperature, tropical, weather

General General

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models.

In PloS one ; h5-index 176.0

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

Pourghasemi Hamid Reza, Pouyan Soheila, Farajzadeh Zakariya, Sadhasivam Nitheshnirmal, Heidari Bahram, Babaei Sedigheh, Tiefenbacher John P


Radiology Radiology

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

In PloS one ; h5-index 176.0

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0-3) and geographic extent (0-4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.

Zhu Jocelyn, Shen Beiyi, Abbasi Almas, Hoshmand-Kochi Mahsa, Li Haifang, Duong Tim Q


General General

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.

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

The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

Agbehadji Israel Edem, Awuzie Bankole Osita, Ngowi Alfred Beati, Millham Richard C


2019 novel coronavirus disease (COVID-19), artificial intelligence (AI), big data, contact tracing, nature-inspired computing (NIC)

General General

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models.

In Chemosphere

The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 μg/m3 (range: 7.00-73.40 μg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.

Li Huiming, Dai Qian’ying, Yang Meng, Li Fengying, Liu Xuemei, Zhou Mengfan, Qian Xin


Airborne particle-bound metals, Back propagation artificial neural network, Health risk, Simulation, Support vector machine

General General

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.

In Cell systems

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here:

Liu Ge, Carter Brandon, Bricken Trenton, Jain Siddhartha, Viard Mathias, Carrington Mary, Gifford David K


COVID-19, SARS-CoV-2, combinatorial optimization, haplotype, machine learning, major histocompatibility complex, peptide vaccine, population coverage, vaccine augmentation, vaccine evaluation

General General

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm.

In Frontiers in public health

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.

Iwendi Celestine, Bashir Ali Kashif, Peshkar Atharva, Sujatha R, Chatterjee Jyotir Moy, Pasupuleti Swetha, Mishra Rishita, Pillai Sofia, Jo Ohyun


COVID-19, boosting, healthcare analytics, infection, patient data, random forest classification

General General

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

In Frontiers in immunology ; h5-index 100.0

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

Ong Edison, Wong Mei U, Huffman Anthony, He Yongqun


COVID-19, S protein, machine learning, non-structural protein 3, reverse vaccinology, vaccine, vaxign, vaxign-ML

General General

Lung involvement in macrophage activation syndrome and severe COVID-19: results from a cross-sectional study to assess clinical, laboratory and artificial intelligence-radiological differences.

In Annals of the rheumatic diseases ; h5-index 121.0

OBJECTIVES : To evaluate the clinical pictures, laboratory tests and imaging of patients with lung involvement, either from severe COVID-19 or macrophage activation syndrome (MAS), in order to assess how similar these two diseases are.

METHODS : The present work has been designed as a cross-sectional single-centre study to compare characteristics of patients with lung involvement either from MAS or severe COVID-19. Chest CT scans were assessed by using an artificial intelligence (AI)-based software.

RESULTS : Ten patients with MAS and 47 patients with severe COVID-19 with lung involvement were assessed. Although all patients showed fever and dyspnoea, patients with MAS were characterised by thrombocytopaenia, whereas patients with severe COVID-19 were characterised by lymphopaenia and neutrophilia. Higher values of H-score characterised patients with MAS when compared with severe COVID-19. AI-reconstructed images of chest CT scan showed that apical, basal, peripheral and bilateral distributions of ground-glass opacities (GGOs), as well as apical consolidations, were more represented in severe COVID-19 than in MAS. C reactive protein directly correlated with GGOs extension in both diseases. Furthermore, lymphopaenia inversely correlated with GGOs extension in severe COVID-19.

CONCLUSIONS : Our data could suggest laboratory and radiological differences between MAS and severe COVID-19, paving the way for further hypotheses to be investigated in future confirmatory studies.

Ruscitti Piero, Bruno Federico, Berardicurti Onorina, Acanfora Chiara, Pavlych Viktoriya, Palumbo Pierpaolo, Conforti Alessandro, Carubbi Francesco, Di Cola Ilenia, Di Benedetto Paola, Cipriani Paola, Grassi Davide, Masciocchi Carlo, Iagnocco Annamaria, Barile Antonio, Giacomelli Roberto


arthritis, juvenile, inflammation, “stills disease, adult-onset”

General General

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

In Big data

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.

Martínez-Álvarez F, Asencio-Cortés G, Torres J F, Gutiérrez-Avilés D, Melgar-García L, Pérez-Chacón R, Rubio-Escudero C, Riquelme J C, Troncoso A


big data, coronavirus, deep learning, metaheuristics, soft computing

Cardiology Cardiology

COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges.

In Gastroenterology research and practice

Background : The novel coronavirus SARS-CoV-2 causing COVID-19 disease is yielding a global outbreak with severe threats to public health. In this paper, we aimed at reviewing the current knowledge about COVID-19 infectious risk status in inflammatory bowel disease (IBD) patients requiring immunosuppressive medication. We also focused on several molecular insights that could explain why IBD patients appear not to have higher risks of infection and worse outcomes in COVID-19 than the general population in an attempt to provide scientific support for safer decisions in IBD patient care.

Methods : PubMed electronic database was interrogated for relevant articles involving data about common molecular pathways and shared treatment strategies between SARS-CoV-2, SARS-CoV-1, MERS-CoV, and inflammatory bowel diseases. Besides, Neural Covidex, an artificial intelligence tool, was used to answer queries about pathogenic coronaviruses and possible IBD interactions using the COVID-19 Open Research Dataset (CORD-19). Discussions. Few molecular and therapeutic interactions between IBD and pathogenic coronaviruses were explored. First, we showed how the activity of soluble angiotensin-converting enzyme 2, CD209L other receptors, and phosphorylated α subunit of eukaryotic translation initiation factor 2 might exert protective impact in IBD in case of coronavirus infection. Second, IBD medication was discussed in the context of possible beneficial effects on COVID-19 pathogeny, including "cytokine storm" prevention and treatment, immunomodulation, interferon signaling blocking, and viral endocytosis inhibition.

Conclusions : Using the current understanding of SARS-CoV-2 as well as other pathogenic coronaviruses immunopathology, we showed why IBD patients should not be considered at an increased risk of infection or more severe outcomes. Whether our findings are entirely applicable to the pathogenesis, disease susceptibility, and treatment management of SARS-CoV-2 infection in IBD must be further explored.

Popa Iolanda Valentina, Diculescu Mircea, Mihai Cătălina, Cijevschi-Prelipcean Cristina, Burlacu Alexandru


General General

Summoning a New Artificial Intelligence Patent Model: In the Age of Pandemic.


To combat the fast-moving spread of the pandemic we need an equally speedy and powerful tool. On the forefront against COVID-19, for example, AI technology has become a digital armament in the development of new drugs, vaccines, diagnostic methods, and forecasting programs. Patenting these new, nonobvious, and efficient technological solutions is a critical step in fostering the research and development, the huge investments as well as the commercial processes. This article considers the challenges of the current patent law as they apply to AI inventions in general and especially in the age of a global pandemic. The article proposes a novel solution to the hurdles of patenting AI technology by establishing a new patent track model for AI inventions (including the inventions that are made by AI systems and creative AI systems themselves). Unlike other publications promoting either complete abandonment of AI related patents, or advocating to maintain current patent laws, or recommending minor adjustment to patent laws, this article suggests a novel model of separate patent venue solely targeting AI inventions. The argument of this article is based on four pillars: the difficulty of having a patent-eligible subject matter, the hurdle of the "blackbox" conundrum, the confusion of who is "a person of ordinary skills in the art" ("POSITA"), and the criticality of establishing a new AI patent track model, a crucial step, especially during a global epidemic. The first pillar of the argument is the difficulty of having a patent-eligible subject matter in AI inventions. We therefore propose the new AI patent track model that would extend the scope of patent protection to cover creative AI systems, including both the algorithms and trained models, and AI-made inventions in order to, inter alia, incentivize investments of the "Multi-Players". The second pillar of the argument of the argument is the hurdle posed by the "blackbox" conundrum of AI systems that undermines the explainability and transparency of the inventions. In analogy to already existing rules applied to microorganism patents that are hard to describe, we advise a depository rule for AI working models to sufficiently describe the otherwise inexplicable inventions. The third pillar arises from the confusion of who is a person of ordinary skills in regard to the nonobviousness assessment of AI inventions. We submit an alternative standard of "a skilled person using an ordinary AI tool in the art" under the new track model to enable the evaluation of the patentability of complex AI inventions. The fourth pillar of the argument is the criticality of establishing a new AI patent track model on the grounds that the current patent law regime has posed substantial hurdles and uncertainties for patenting AI inventions with regard to almost all patentability requirements. We analyzed each of the requirements to demonstrate that most, if not all, aspects of patent law are not suitable in the AI era; only a revolutionary new patent model specific for AI inventions could solve all the concerns while maintaining the patent incentive for innovations. Our model also suggests an expedited examination with the aid of AI tools and a shortened patent lifetime in light of the fast AI development and technology elimination speed. The article concludes with the hope to harness AI technology for the wellbeing of humanity, in general and especially during tough times in the current COVID-19 era and in general.

Yanisky-Ravid Shlomit, Jin Regina


Advanced Technology: Digital Era, Artificial Intelligence, COVID-19, Incentive, Intellectual Property, Investments, Law and Economics, Pandemic, Patent

Public Health Public Health

The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales.


A novel coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 240,000 cases of COVID-19 worldwide as of March 19, 2020. Previous studies have supported an epidemiological hypothesis that cold and dry environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid environments see attenuated viral transmission (e.g., influenza). However, the role of temperature and humidity in transmission of COVID-19 has not yet been established. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case count without the implementation of extensive public health interventions.

Poirier Canelle, Luo Wei, Majumder Maimuna S, Liu Dianbo, Mandl Kenneth, Mooring Todd, Santillana Mauricio


COVID-19, coronavirus Wuhan, digital epidemiology, machine learning, machine learning in public health, modeling disease outbreaks, precision public health

General General

Mutations Strengthened SARS-CoV-2 Infectivity.

In Journal of molecular biology ; h5-index 65.0

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-COV-2 infectivity is very difficult owing to its continuous evolution with over ten thousand single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 (ACE2) receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 sub- types have become slightly more infectious, while other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-ACE2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain (RBD), we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif (RBM), i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.

Chen Jiahui, Wang Rui, Wang Menglun, Wei Guo-Wei


Binding free energy change, Deep learning, Mutation, Persistent homology, Protein–protein interaction, Viral infectivity

oncology Oncology

Patient-reported Outcomes of Patients With Breast Cancer During the COVID-19 Outbreak in the Epicenter of China: A Cross-sectional Survey Study.

In Clinical breast cancer

INTRODUCTION : We aimed to analyze the psychological status in patients with breast cancer (BC) in the epicenter of the coronavirus disease 2019 (COVID-19) pandemic.

PATIENTS AND METHODS : A total of 658 individuals were recruited from multiple BC centers in Hubei Province. Online questionnaires were conducted, and these included demographic information, clinical features, and 4 patient-reported outcome scales (Generalized Anxiety Disorder Questionnaire [GAD-7], Patient Health Questionnaire [PHQ-9], Insomnia Severity Index [ISI], and Impact of Events Scale-Revised [IES-R]). Multivariable logistic regression analysis was designed to identify potential factors on mental health outcomes.

RESULTS : Questionnaires were collected from February 16, 2020 to February 19, 2020, the peak time point of the COVID-19 outbreak in China. Of patients with BC, 46.2% had to modify planned necessary anti-cancer treatment during the outbreak. Severe anxiety and severe depression were reported by 8.9% and 9.3% of patients, respectively. Severe distress and insomnia were reported by 20.8% and 4.0% of patients, respectively. Multivariable logistic regression analysis demonstrated poor general condition, shorter duration after BC diagnosis, aggressive BC molecular subtypes, and close contact with patients with COVID-19 as independent factors associated with anxiety. Poor general condition and central venous catheter flushing delay were factors that were independently associated with depression. In terms of insomnia, poor generation condition was the only associated independent factor. Poor physical condition and treatment discontinuation were underlying risk factors for distress based on multivariable analysis.

CONCLUSION : High rates of anxiety, depression, distress, and insomnia were observed in patients with BC during the COVID-19 outbreak. Special attention should be paid to the psychological status of patients with BC, especially those with poor general condition, treatment discontinuation, aggressive molecular subtypes, and metastatic BC.

Juanjuan Li, Santa-Maria Cesar Augusto, Hongfang Feng, Lingcheng Wang, Pengcheng Zhang, Yuanbing Xu, Yuyan Tan, Zhongchun Liu, Bo Du, Meng Lan, Qingfeng Yang, Feng Yao, Yi Tu, Shengrong Sun, Xingrui Li, Chuang Chen


Breast cancer, COVID-19, Epicenter, Psychological status

General General

Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US.

In The American journal of the medical sciences

BACKGROUND : There has been much interest in environmental temperature and race as modulators of Coronavirus disease-19 (COVID-19) infection and mortality. However, in the United States race and temperature correlate with various other social determinants of health, comorbidities, and environmental influences that could be responsible for noted effects. This study investigates the independent effects of race and environmental temperature on COVID-19 incidence and mortality in United States counties.

METHODS : Data on COVID-19 and risk factors in all United States counties was collected. 661 counties with at least 50 COVID-19 cases and 217 with at least 10 deaths were included in analyses. Upper and lower quartiles for cases/100,000 people and halves for deaths/100,000 people were compared with t-tests. Adjusted linear and logistic regression analyses were performed to evaluate the independent effects of race and environmental temperature.

RESULTS : Multivariate regression analyses demonstrated Black race is a risk factor for increased COVID-19 cases (OR=1.22, 95% CI: 1.09-1.40, P=0.001) and deaths independent of comorbidities, poverty, access to health care, and other risk factors. Higher environmental temperature independently reduced caseload (OR=0.81, 95% CI: 0.71-0.91, P=0.0009), but not deaths.

CONCLUSIONS : Higher environmental temperatures correlated with reduced COVID-19 cases, but this benefit does not yet appear in mortality models. Black race was an independent risk factor for increased COVID-19 cases and deaths. Thus, many proposed mechanisms through which Black race might increase risk for COVID-19, such as socioeconomic and healthcare-related predispositions, are inadequate in explaining the full magnitude of this health disparity.

Li Adam Y, Hannah Theodore C, Durbin John R, Dreher Nickolas, McAuley Fiona M, Marayati Naoum Fares, Spiera Zachary, Ali Muhammad, Gometz Alex, Kostman J T, Choudhri Tanvir F


Black Race, COVID-19, Coronavirus, Environmental temperature, SARS-CoV-2

Radiology Radiology

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.

In Viruses ; h5-index 58.0

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Sedik Ahmed, Iliyasu Abdullah M, Abd El-Rahiem Basma, Abdel Samea Mohammed E, Abdel-Raheem Asmaa, Hammad Mohamed, Peng Jialiang, Abd El-Samie Fathi E, Abd El-Latif Ahmed A


CNN, COVID-19, Corona virus, LSTM networks, deep learning, image processing, machine learning

General General

CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from Chest X-ray

ArXiv Preprint

The recent outbreak of COVID-19 has halted the whole world, bringing a devastating effect on public health, global economy, and educational systems. As the vaccine of the virus is still not available, the most effective way to combat the virus is testing and social distancing. Among all other detection techniques, the Chest X-ray (CXR) based method can be a good solution for its simplicity, rapidity, cost, efficiency, and accessibility. In this paper, we propose CovMUNET, which is a multiple loss deep neural network approach to detect COVID-19 cases from CXR images. Extensive experiments are performed to ensure the robustness of the proposed algorithm and the performance is evaluated in terms of precision, recall, accuracy, and F1-score. The proposed method outperforms the state-of-the-art approaches with an accuracy of 96.97% for 3-class classification (COVID-19 vs normal vs pneumonia) and 99.41% for 2-class classification (COVID vs non-COVID). The proposed neural architecture also successfully detects the abnormality in CXR images.

A. Q. M. Sazzad Sayyed, Dipayan Saha, Abdul Rakib Hossain


General General

Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.

In Environmental science and pollution research international

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.

Sharma Sachin


COVID-19, Computed tomography (CT) scan, Coronavirus, Machine learning, Pneumonia, Polymerase chain reaction (PCR)

Cardiology Cardiology

Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial.

In BMJ open

INTRODUCTION : There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID-19 exposure is being quarantined, it is necessary to perform temperature and symptom surveillance. As such surveillance is intermittent in nature and highly dependent on self-discipline, it has limited effectiveness. Advances in biosensor technologies made it possible to continuously monitor physiological parameters using wearable biosensors with a variety of form factors.

OBJECTIVE : To explore the potential of using wearable biosensors to continuously monitor multidimensional physiological parameters for early detection of COVID-19 clinical progression.

METHOD : This randomised controlled open-labelled trial will involve 200-1000 asymptomatic subjects with close COVID-19 contact under mandatory quarantine at designated facilities in Hong Kong. Subjects will be randomised to receive a remote monitoring strategy (intervention group) or standard strategy (control group) in a 1:1 ratio during the 14 day-quarantine period. In addition to fever and symptom surveillance in the control group, subjects in the intervention group will wear wearable biosensors on their arms to continuously monitor skin temperature, respiratory rate, blood pressure, pulse rate, blood oxygen saturation and daily activities. These physiological parameters will be transferred in real time to a smartphone application called Biovitals Sentinel. These data will then be processed using a cloud-based multivariate physiology analytics engine called Biovitals to detect subtle physiological changes. The results will be displayed on a web-based dashboard for clinicians' review. The primary outcome is the time to diagnosis of COVID-19.

ETHICS AND DISSEMINATION : Ethical approval has been obtained from institutional review boards at the study sites. Results will be published in peer-reviewed journals.

Wong Chun Ka, Ho Deborah Tip Yin, Tam Anthony Raymond, Zhou Mi, Lau Yuk Ming, Tang Milky Oi Yan, Tong Raymond Cheuk Fung, Rajput Kuldeep Singh, Chen Gengbo, Chan Soon Chee, Siu Chung Wah, Hung Ivan Fan Ngai


infection control, telemedicine, virology

General General

Tiotropium is Predicted to be a Promising Drug for COVID-19 Through Transcriptome-Based Comprehensive Molecular Pathway Analysis.

In Viruses ; h5-index 58.0

The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects almost everyone in the world in many ways. We previously predicted antivirals (atazanavir, remdesivir and lopinavir/ritonavir) and non-antiviral drugs (tiotropium and rapamycin) that may inhibit the replication complex of SARS-CoV-2 using our molecular transformer-drug target interaction (MT-DTI) deep-learning-based drug-target affinity prediction model. In this study, we dissected molecular pathways upregulated in SARS-CoV-2-infected normal human bronchial epithelial (NHBE) cells by analyzing an RNA-seq data set with various bioinformatics approaches, such as gene ontology, protein-protein interaction-based network and gene set enrichment analyses. The results indicated that the SARS-CoV-2 infection strongly activates TNF and NFκB-signaling pathways through significant upregulation of the TNF, IL1B, IL6, IL8, NFKB1, NFKB2 and RELB genes. In addition to these pathways, lung fibrosis, keratinization/cornification, rheumatoid arthritis, and negative regulation of interferon-gamma production pathways were also significantly upregulated. We observed that these pathologic features of SARS-CoV-2 are similar to those observed in patients with chronic obstructive pulmonary disease (COPD). Intriguingly, tiotropium, as predicted by MT-DTI, is currently used as a therapeutic intervention in COPD patients. Treatment with tiotropium has been shown to improve pulmonary function by alleviating airway inflammation. Accordingly, a literature search summarized that tiotropium reduced expressions of IL1B, IL6, IL8, RELA, NFKB1 and TNF in vitro or in vivo, and many of them have been known to be deregulated in COPD patients. These results suggest that COVID-19 is similar to an acute mode of COPD caused by the SARS-CoV-2 infection, and therefore tiotropium may be effective for COVID-19 patients.

Kang Keunsoo, Kim Hoo Hyun, Choi Yoonjung


COPD, COVID-19, RNA-seq, SARS-CoV-2, molecular pathways, tiotropium

General General

COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net

ArXiv Preprint

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of mid-July 2020, more than 12 million people were infected, and more than 570,000 death were reported. Computed Tomography (CT) images can be used as an alternative to the time-consuming RT-PCR test, to detect COVID-19. In this work we propose a segmentation framework to detect chest regions in CT images, which are infected by COVID-19. We use an architecture similar to U-Net model, and train it to detect ground glass regions, on pixel level. As the infected regions tend to form a connected component (rather than randomly distributed pixels), we add a suitable regularization term to the loss function, to promote connectivity of the segmentation map for COVID-19 pixels. 2D-anisotropic total-variation is used for this purpose, and therefore the proposed model is called "TV-UNet". Through experimental results on a relatively large-scale CT segmentation dataset of around 900 images, we show that adding this new regularization term leads to 2\% gain on overall segmentation performance compared to the U-Net model. Our experimental analysis, ranging from visual evaluation of the predicted segmentation results to quantitative assessment of segmentation performance (precision, recall, Dice score, and mIoU) demonstrated great ability to identify COVID-19 associated regions of the lungs, achieving a mIoU rate of over 99\%, and a Dice score of around 86\%.

Narges Saeedizadeh, Shervin Minaee, Rahele Kafieh, Shakib Yazdani, Milan Sonka


Ophthalmology Ophthalmology

Artificial intelligence in ophthalmology during COVID-19 and in the post COVID-19 era.

In Current opinion in ophthalmology

PURPOSE OF REVIEW : To highlight artificial intelligence applications in ophthalmology during the COVID-19 pandemic that can be used to: describe ocular findings and changes correlated with COVID-19; extract information from scholarly articles on SARS-CoV-2 and COVID-19 specific to ophthalmology; and implement efficient patient triage and telemedicine care.

RECENT FINDINGS : Ophthalmology has been leading in artificial intelligence and technology applications. With medical imaging analysis, pixel-annotated distinguishable features on COVID-19 patients may help with noninvasive diagnosis and severity outcome predictions. Using natural language processing (NLP) and data integration methods, topic modeling on more than 200 ophthalmology-related articles on COVID-19 can summarize ocular manifestations, viral transmission, treatment strategies, and patient care and practice management. Artificial intelligence for telemedicine applications can address the high demand, prioritize and triage patients, as well as improve at home-monitoring devices and secure data transfers.

SUMMARY : COVID-19 is significantly impacting the way we are delivering healthcare. Given the already successful implementation of artificial intelligence applications and telemedicine in ophthalmology, we expect that these systems will be embraced more as tools for research, education, and patient care.

Hallak Joelle A, Scanzera Angel, Azar Dimitri T, Chan R V Paul


General General

Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research.

In PloS one ; h5-index 176.0

This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.

Fry Caroline V, Cai Xiaojing, Zhang Yi, Wagner Caroline S


General General

Virtual care: Enhancing access or harming care?

In Healthcare management forum

COVID-19 has catalyzed the adoption of virtual medical care in Canada. Virtual care can improve access to healthcare services, particularly for those in remote locations or with health conditions that make seeing a doctor in person difficult or unsafe. However, virtual walk-in clinic models that do not connect patients with their own doctors can lead to fragmented, lower quality care. Although virtual walk-in clinics can be helpful for those who temporarily lack access to a family doctor, they should not be relied on as a long-term substitute to an established relationship with a primary care provider. Virtual care also raises significant privacy issues that policy-makers must address prior to implementing these models. Patients should be cautious of the artificial intelligence recommendations generated by some virtual care applications, which have been linked to quality of care concerns.

Hardcastle Lorian, Ogbogu Ubaka


General General

Germany's digital health reforms in the COVID-19 era: lessons and opportunities for other countries.

In NPJ digital medicine

Reimbursement is a key challenge for many new digital health solutions, whose importance and value have been highlighted and expanded by the current COVID-19 pandemic. Germany's new Digital Healthcare Act (Digitale-Versorgung-Gesetz or DVG) entitles all individuals covered by statutory health insurance to reimbursement for certain digital health applications (i.e., insurers will pay for their use). Since Germany, like the United States (US), is a multi-payer health care system, the new Act provides a particularly interesting case study for US policymakers. We first provide an overview of the new German DVG and outline the landscape for reimbursement of digital health solutions in the US, including recent changes to policies governing telehealth during the COVID-19 pandemic. We then discuss challenges and unanswered questions raised by the DVG, ranging from the limited scope of the Act to privacy issues. Lastly, we highlight early lessons and opportunities for other countries.

Gerke Sara, Stern Ariel D, Minssen Timo


Health policy, Health services

General General

DeepTracer: Automated Protein Complex Structure Prediction from CoV-related Cryo-EM Density Maps

bioRxiv Preprint

Information about the macromolecular structure of viral protein complexes such as SARS-CoV-2, and the related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automatic deep learning-based method for de novo multi-chain protein complex structure prediction from high-resolution cryo-electron microscopy (cryo-EM) density maps. We applied DeepTracer on a set of 62 coronavirus-related raw experimental density maps, among them 10 with no existing deposited model structure. We observed an average residue match of 84% with the deposited structures and an average RMSD of 0.93[A]. Larger comparative tests further exemplify DeepTracer's competitive accuracy and efficiency of multi-chain all atom complex structure prediction, with the ability of tracing around 60,000 residues within two hours. The web service and prediction results are globally accessible at

Pfab, J.; Phan, N. M.; Si, D.


General General

Towards explainable deep neural networks (xDNN).

In Neural networks : the official journal of the International Neural Network Society

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers an explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds). The proposed approach, xDNN is using prototypes. Prototypes are actual training data samples (images), which are local peaks of the empirical data distribution called typicality as well as of the data density. This generative model is identified in a closed form and equates to the pdf but is derived automatically and entirely from the training data with no user- or problem-specific thresholds, parameters or intervention. The proposed xDNN offers a new deep learning architecture that combines reasoning and learning in a synergy. It is non-iterative and non-parametric, which explains its efficiency in terms of time and computational resources. From the user perspective, the proposed approach is clearly understandable to human users. We tested it on challenging problems as the classification of different lighting conditions for driving scenes (iROADS), object detection (Caltech-256, and Caltech-101), and SARS-CoV-2 identification via computed tomography scan (COVID CT-scans dataset). xDNN outperforms the other methods including deep learning in terms of accuracy, time to train and offers an explainable classifier.

Angelov Plamen, Soares Eduardo


Deep-learning, Explainable AI, Interpretability, Prototype-based models

Public Health Public Health

The reproduction number of COVID-19 and its correlation with public health interventions.

In medRxiv : the preprint server for health sciences

Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22+/-1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67+/-0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24+/-2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.

Linka Kevin, Peirlinck Mathias, Kuhl Ellen


Radiology Radiology

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: Three case reports.

In Journal of X-ray science and technology

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.

Su Ying, Han Yi, Liu Jie, Qiu Yue, Tan Qian, Zhou Zhen, Yu Yi-Zhou, Chen Jun, Giger Maryellen L, Lure Fleming Y M, Luo Zhe


Coronavirus, computerized tomography, image analysis using artificial intelligence, pneumonia, steroids, treating COVID-19 patients

General General

RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus.

In Cell systems

SARS-CoV-2 genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery. Subcellular localization of its viral RNA could, thus, play important roles in viral replication and host antiviral immune response. We perform computational modeling of SARS-CoV-2 viral RNA subcellular residency across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes with the human transcriptome and other coronaviruses. We predict the SARS-CoV-2 RNA genome and sgRNAs to be enriched toward the host mitochondrial matrix and nucleolus, and that the 5' and 3' viral untranslated regions contain the strongest, most distinct localization signals. We interpret the mitochondrial residency signal as an indicator of intracellular RNA trafficking with respect to double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Wu Kevin E, Fazal Furqan M, Parker Kevin R, Zou James, Chang Howard Y


APEX-seq, COX4, SARS-CoV-2, double-membrane vesicle, hypothesis generation, machine learning model, proximity labelling, viral RNA localization

Public Health Public Health

Early triage of critically ill COVID-19 patients using deep learning.

In Nature communications ; h5-index 260.0

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

Liang Wenhua, Yao Jianhua, Chen Ailan, Lv Qingquan, Zanin Mark, Liu Jun, Wong SookSan, Li Yimin, Lu Jiatao, Liang Hengrui, Chen Guoqiang, Guo Haiyan, Guo Jun, Zhou Rong, Ou Limin, Zhou Niyun, Chen Hanbo, Yang Fan, Han Xiao, Huan Wenjing, Tang Weimin, Guan Weijie, Chen Zisheng, Zhao Yi, Sang Ling, Xu Yuanda, Wang Wei, Li Shiyue, Lu Ligong, Zhang Nuofu, Zhong Nanshan, Huang Junzhou, He Jianxing


General General

Commentary: An integrated blueprint for digital mental health services amidst COVID-19.

In JMIR mental health

In-person traditional approaches to mental health care services are facing difficulties amidst the COVID-19 crisis. Recent social distancing has revamped the attention on non-traditional mental health care delivery to overcome the difficult access to services and fill the void. Tele-health has been established for several decades but has only been able to fill a small gap in services. Mental health mobile and tele-digital health complements are well poised to respond to the upsurge of COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first aid resources and targeting interventions. Of concern is the rigorous evaluation of these new opportunities in terms of quality of interventions, effectiveness and confidentiality. Service delivery could be broadened to include trained unlicensed professionals whom may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members to adjust to stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking of mental health (with real-time automation and machine learning) in general populations as well as consideration and initiatives for underserved and vulnerable subpopulations.

Balcombe Luke, De Leo Diego


Radiology Radiology

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

BACKGROUND : The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia.

METHODS : A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans.

RESULTS : CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia.

CONCLUSION : Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

Zhang Hai-Tao, Zhang Jin-Song, Zhang Hai-Hua, Nan Yan-Dong, Zhao Ying, Fu En-Qing, Xie Yong-Hong, Liu Wei, Li Wang-Ping, Zhang Hong-Jun, Jiang Hua, Li Chun-Mei, Li Yan-Yan, Ma Rui-Na, Dang Shao-Kang, Gao Bo-Bo, Zhang Xi-Jing, Zhang Tao


2019 novel coronavirus, Artificial intelligence (AI), Computed tomography (CT), Ground glass opacity (GGO), Viral pneumonia

Cardiology Cardiology

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

In Bioelectronic medicine

Background : The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information.

Main body : While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models.

Conclusion : This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

Debnath Shubham, Barnaby Douglas P, Coppa Kevin, Makhnevich Alexander, Kim Eun Ji, Chatterjee Saurav, Tóth Viktor, Levy Todd J, Paradis Marc D, Cohen Stuart L, Hirsch Jamie S, Zanos Theodoros P


Artificial intelligence (AI), Clinical decision-making, Coronavirus disease 19 (COVID-19), Healthcare, Machine learning (ML)

General General

The combination of artificial intelligence and systems biology for intelligent vaccine design.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED : This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION : Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Russo Giulia, Reche Pedro, Pennisi Marzio, Pappalardo Francesco


Covid-19, Vaccine development, agent-based models, artificial intelligence, epitope prediction, immune system modeling, systems biology

General General

Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide

bioRxiv Preprint

A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspective for treatment. We developed alignment-free machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of potential human-adapted coronavirus using genomic nucleotide. We performed extensive experiments through six different feature transformation and machine learning algorithms in combination with digital signal processing to infer the lethality of possible future novel coronaviruses using previous existing strains. The results tested on SARS-CoV, MERS-Cov and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our study achieves high levels of prediction performance based on raw RNA sequences alone without genome annotations and specialized biological knowledge. The results demonstrate that, for any novel human coronavirus strains, this alignment-free machine learning-based approach can offer a reliable real-time estimation for its viral lethality.

YIN, R.; Luo, Z.; Kwoh, C. K.


General General

[Information technology and digital health to support health in the time of CoViD-19.]

In Recenti progressi in medicina

The CoViD-19 pandemic has provided the opportunity for the health care's digital revolution with the unprecedented accelerated expansion of telehealth, telemedicine and other digital health tools. Several tools have been developed and launched at national and international level to face the emergency, including tools to perform online triage, symptoms checking, video visits and remote monitoring, and to conduct local and national epidemiological surveillance studies. Artificial intelligence-based tools have also been developed to diagnose cases of CoViD-19 or to identify patients at risk. Most of these technologies have been endorsed by medical societies such as the American Medical Association and the American Academy of Family Physicians which launched specific guidelines about their use. The growth in telemedicine services and in digital health technologies could not have occurred without important telehealth regulatory changes that have occurred in some countries aimed at promoting their use to face the CoViD-19 emergency, such as the deregulation of the use of video conferencing and video chat systems to carry out video visits, and the payment parity between telehealth and in clinic care. In order to decide whether to continue using these tools even after the pandemic is over, it could be useful to perform validation and efficacy studies of these tools to study their implications on the doctor-patient relationship, to understand if the new features can be integrated with the other technological tools already in use, and if they can improve clinical practice and quality of care.

Santoro Eugenio

General General

CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization.

In Computers in biology and medicine

With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic. All the architectures are made publicly available at:

Mahmud Tanvir, Rahman Md Awsafur, Fattah Shaikh Anowarul


COVID-19 diagnosis, Imaging informatics, Neural network, Pneumonia diagnosis, Transfer learning, X-ray

General General

Proteomics and informatics for understanding phases and identifying biomarkers in COVID-19 disease.

In Journal of proteome research

The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that a fuller definition of patient status, trajectory, sequelae and responses to therapy is now required. There is accumulating evidence - from studies of both COVID-19 and the related disease SARS - that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase) and the inflammatory response (e.g. C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. In order to be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g. using mass spectrometry) but also informatics approaches via which to derive actionable information from large data-sets. Here we review applications of proteomics to COVID-19 and SARS, and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

Whetton Anthony D, Preston George W, Abubeker Semira, Geifman Nophar


Internal Medicine Internal Medicine

Information and Misinformation on COVID-19: a Cross-Sectional Survey Study.

In Journal of Korean medical science

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has led to a large volume of publications, a barrage of non-reviewed preprints on various professional repositories and a slew of retractions in a short amount of time.

METHODS : We conducted an e-survey using a cloud-based website to gauge the potential sources of trustworthy information and misinformation and analyzed researchers', clinicians', and academics' attitude toward unpublished items, and pre- and post-publication quality checks in this challenging time.

RESULTS : Among 128 respondents (mean age, 43.2 years; M:F, 1.1:1), 60 (46.9%) were scholarly journal editors and editorial board members. Social media channels were distinguished as the most important sources of information as well as misinformation (81 [63.3%] and 86 [67.2%]). Nearly two in five (62, 48.4%) respondents blamed reviewers, editors, and misinterpretation by readers as additional contributors alongside authors for misinformation. A higher risk of plagiarism was perceived by the majority (70, 58.6%), especially plagiarism of ideas (64.1%) followed by inappropriate paraphrasing (54.7%). Opinion was divided on the utility of preprints for changing practice and changing retraction rates during the pandemic period, and higher rejections were not supported by most (76.6%) while the importance of peer review was agreed upon by a majority (80, 62.5%). More stringent screening by journal editors (61.7%), and facilitating open access plagiarism software (59.4%), including Artificial Intelligence (AI)-based algorithms (43.8%) were among the suggested solutions. Most (74.2%) supported the need to launch a specialist bibliographic database for COVID-19, with information indexed (62.3%), available as open-access (82.8%), after expanding search terms (52.3%) and following due verification by academics (66.4%), and journal editors (52.3%).

CONCLUSION : While identifying social media as a potential source of misinformation on COVID-19, and a perceived high risk of plagiarism, more stringent peer review and skilled post-publication promotion are advisable. Journal editors should play a more active role in streamlining publication and promotion of trustworthy information on COVID-19.

Gupta Latika, Gasparyan Armen Yuri, Misra Durga Prasanna, Agarwal Vikas, Zimba Olena, Yessirkepov Marlen


COVID-19, Coronavirus Disease 2019, Information, Periodicals as Topic, Publishing, Social Media

Public Health Public Health

Emerging coronavirus diseases and future perspectives.

In Virusdisease

Coronavirus related infectious diseases seems to be biggest challenge of 21 century that have been constantly emerging and threating public health around the globe. Coronavirus disease-19 (COVID-19) that was detected as cause of respiratory tract infection in China by end the December 2019 impelled World Health Organization to declare in January 2020 public health emergency of international concern and consequently pandemic in March 2020. Over a past six months COVID-19 pandemic has wrapped up all continents except Antarctica. Scientists around the globe are finding way to tackle and reduce the ultimate risk and size of pandemic with lower morbidity and mortality rates. In this context, technologies such as sequencing, Crispr and artificial intelligence are playing vital role in diagnosis and management of infectious disease in contrast to conventional methods. Despite of this, there is a need to have rapid and early diagnostic tools and systems that recognize infectious disease in asymptotic condition. Here we provide an overview on the recent CoV outbreak and contribution of technologies with the emphasis on the future management for detection of such infectious diseases.

Akhter Shireen, Akhtar Shahzeen


Artificial intelligence, COVID-19, CRISPR, Coronavirus, Infectious diseases, Sequencing

General General

Bacillus Calmette-Guérin vaccine, antimalarial, age and gender relation to COVID-19 spread and mortality.

In Vaccine ; h5-index 70.0

COVID-19 is affecting different countries all over the world with great variation in infection rate and death ratio. Some reports suggested a relation between the Bacillus Calmette-Guérin (BCG) vaccine and the malaria treatment to the prevention of SARS-CoV-2 infection. Some reports related infant's lower susceptibility to the COVID-19. Some other reports a higher risk in males compared to females in such COVID-19 pandemic. Also, some other reports claimed the possible use of chloroquine and hydroxychloroquine as prophylactic in such a pandemic. The present commentary is to discuss the possible relation between those factors and SARS-CoV-2 infection.

Osama El-Gendy Ahmed, Saeed Haitham, Ali Ahmed M A, Zawbaa Hossam M, Gomaa Dina, Harb Hadeer S, Madney Yasmin M, Osama Hasnaa, Abdelrahman Mona A, Abdelrahim Mohamed E A


Age, Antimalarial, BCG, COVID 19, Gender

General General

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning.

In Journal of proteome research

There have been more than 2.2 million confirmed cases and over 120,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently lack of proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression, from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.

Zeng Xiangxiang, Song Xiang, Ma Tengfei, Pan Xiaoqin, Zhou Yadi, Hou Yuan, Zhang Zheng, Li Kenli, Karypis George, Cheng Feixiong


Public Health Public Health

CHEST Reviews: Addressing reduced laboratory-based pulmonary function testing during a pandemic.

In Chest ; h5-index 81.0

To reduce the spread of SARS-CoV-2, many pulmonary function testing (PFT) laboratories have been closed or have significantly reduced their testing capacity. As these mitigation strategies may be necessary for the next 6-18 months to prevent recurrent peaks in disease prevalence, fewer objective measurements of lung function will alter the diagnosis and care of patients with chronic respiratory diseases. PFTs, which include spirometry, lung volumes, and diffusion capacity measurement, are essential to the diagnosis and management of patients with asthma, COPD, and other chronic lung conditions. Both traditional and innovative alternatives to conventional testing must now be explored. These may include peak expiratory flow devices, electronic portable spirometers, portable exhaled nitric oxide measurement, airwave oscillometry devices, as well as novel digital health tools such as smartphone microphone spirometers, and mobile health technologies along integration of machine learning approaches. The adoption of some novel approaches may not merely replace but could improve existing management strategies and alter common diagnostic paradigms. With these options come important technical, privacy, ethical, financial, and medicolegal barriers that must be addressed. However, the COVID-19 pandemic also presents a unique opportunity to augment conventional testing by including innovative and emerging approaches to measuring lung function remotely in patients with respiratory disease. The benefits of such an approach have the potential to enhance respiratory care and empower patient self-management well beyond the current global pandemic.

Kouri Andrew, Gupta Samir, Yadollahi Azadeh, Ryan Clodagh M, Gershon Andrea S, To Teresa, Tarlo Susan M, Goldstein Roger S, Chapman Kenneth R, Chow Chung-Wai


Public Health Public Health

How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19.

In Infectious diseases of poverty ; h5-index 31.0

Shenzhen is a city of 22 million people in south China that serves as a financial and trade center for East Asia. The city has extensive ties to Hubei Province, the first reported epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the world. Initial predictions suggested Shenzhen would experience a high number of COVID-19 cases. These predictions have not materialized. As of 31 March 2020 Shenzhen had only 451 confirmed cases of COVID-19. Contact tracing has shown that no cases were the result of community transmission within the city. While Shenzhen did not implement a citywide lockdown like Wuhan, it did put into place a rapid response system first developed after the severe acute respiratory syndrome (SARS) epidemic in 2003. In the wake of the 2003 SARS outbreak, Shenzhen health authority created a network for surveillance and responding to novel respiratory infections, including pneumonia of unknown causes (PUC). The network rapidly detected mass discussion about PUC and immediately deployed emergency preparedness, quarantine for close contacts of PUC. Five early actions (early detection, early reporting, early diagnosis, early isolation, and early treatment) and four centralized responses (centralized coordination by experts, centralized allocation of resources, centralized placement of patients, and centralized provision of treatment) ensured effective prevention and control. Tripartite working teams comprising community cadres, medical personnel and police were formulated to conduct contact tracing at each neighborhood and residential community. Incorporation of mobile technology, big data, and artificial intelligence into COVID-19 response increased accessibility to health services, reduced misinformation and minimized the impact of fake news. Shenzhen's unique experience in successfully controlling the COVID-19 outbreak may be a useful model for countries and regions currently experiencing rapid spread of the virus.

Zou Huachun, Shu Yuelong, Feng Tiejian


COVID-19, China, Control, Outbreak, Shenzhen

General General

Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects.

In Journal of infection and public health

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Albahri O S, Zaidan A A, Albahri A S, Zaidan B B, Abdulkareem Karrar Hameed, Al-Qaysi Z T, Alamoodi A H, Aleesa A M, Chyad M A, Alesa R M, Kem L C, Lakulu Muhammad Modi, Ibrahim A B, Rashid Nazre Abdul


Artificial intelligence, Benchmarking, COVID-19, Decision-making, Evaluation, MCDA, Medical image

Radiology Radiology

2020 ACR Presidential Address: Quality, Ownership, and Our Role as Physicians.

In Journal of the American College of Radiology : JACR

A story from long ago reminds us of the importance of quality in our practices, of taking ownership of our patients, and of our role as physicians. The coronavirus disease 2019 (COVID-19) pandemic has disrupted our practices. Before the pandemic, many practices were stretched thin by the amount of work that needed to be done. The work stoppage in many locations brought an unwelcome pause but gives us time to reflect on our practices. How can we maintain quality when high volumes return? The role of artificial intelligence, and our role in its development, needs to be considered. At the same time, we need to take more ownership of the patient and be more help to our referring providers. Our own ACR staff are great examples of taking ownership. Finally, we must recognize that patients and their families are important for optimal patient care. Making that connection is significant. Let us we start where we began-in the service of our patients as their physicians. This role is rewarding and, together with a focus on quality and taking ownership, can lead to successful practices that are good for everyone, including ourselves.

Monticciolo Debra L


ACR, artificial intelligence, ownership, presidential address, quality, radiologist as physicians

Ophthalmology Ophthalmology

Advances in Telemedicine in Ophthalmology.

In Seminars in ophthalmology

Telemedicine is the provision of healthcare-related services from a distance and is poised to move healthcare from the physician's office back into the patient's home. The field of ophthalmology is often at the forefront of technological advances in medicine including telemedicine and the use of artificial intelligence. Multiple studies have demonstrated the reliability of tele-ophthalmology for use in screening and diagnostics and have demonstrated benefits to patients, physicians, as well as payors. There remain obstacles to widespread implementation, but recent legislation and regulation passed due to the devastating COVID-19 pandemic have helped to reduce some of these barriers. This review describes the current status of tele-ophthalmology in the United States including benefits, hurdles, current programs, technology, and developments in artificial intelligence. With ongoing advances patients may benefit from improved detection and earlier treatment of eye diseases, resulting in better care and improved visual outcomes.

Parikh Deep, Armstrong Grayson, Liou Victor, Husain Deeba


diabetic retinopathy, screening, tele-ophthalmology, telehealth, telemedicine

Public Health Public Health

Point-of-Care Diagnostic Services as an Integral Part of Health Services during the Novel Coronavirus 2019 Era.

In Diagnostics (Basel, Switzerland)

Point-of-care (POC) diagnostic services are commonly associated with pathology laboratory services. This issue presents a holistic approach to POC diagnostics services from a variety of disciplines including pathology, radiological and information technology as well as mobile technology and artificial intelligence. This highlights the need for transdisciplinary collaboration to ensure the efficient development and implementation of point-of-care diagnostics. The advent of the novel coronavirus 2019 (COVID-19) pandemic has prompted rapid advances in the development of new POC diagnostics. Global private and public sector agencies have significantly increased their investment in the development of POC diagnostics. There is no longer a question about the availability and accessibility of POC diagnostics. The question is "how can POC diagnostic services be integrated into health services in way that is useful and acceptable in the COVID-19 era?".

Mashamba-Thompson Tivani P, Drain Paul K


COVID-19 era, healthcare services, point-of-care diagnostics

General General

Application of Artificial Intelligence in COVID-19 drug repurposing.

In Diabetes & metabolic syndrome

BACKGROUND AND AIM : COVID-19 outbreak has created havoc and a quick cure for the disease will be a therapeutic medicine that has usage history in patients to resolve the current pandemic. With technological advancements in Artificial Intelligence (AI) coupled with increased computational power, the AI-empowered drug repurposing can prove beneficial in the COVID-19 scenario.

METHODS : The recent literature is studied and analyzed from various sources such as Scopus, Google Scholar, PubMed, and IEEE Xplore databases. The search terms used are 'COVID-19', ' AI ', and 'Drug Repurposing'.

RESULTS : AI is implemented in the field design through the generation of the learning-prediction model and performs a quick virtual screening to accurately display the output. With a drug-repositioning strategy, AI can quickly detect drugs that can fight against emerging diseases such as COVID-19. This technology has the potential to improve the drug discovery, planning, treatment, and reported outcomes of the COVID-19 patient, being an evidence-based medical tool.

CONCLUSIONS : Thus, there are chances that the application of the AI approach in drug discovery is feasible. With prior usage experiences in patients, few of the old drugs, if shown active against SARS-CoV-2, can be readily applied to treat the COVID-19 patients. With the collaboration of AI with pharmacology, the efficiency of drug repurposing can improve significantly.

Mohanty Sweta, Harun Ai Rashid Md, Mridul Mayank, Mohanty Chandana, Swayamsiddha Swati


Artificial intelligence, COVID-19, Coronavirus, Deep learning, Drug repositioning, Drug repurposing, Machine learning

General General

Update on therapeutic approaches and emerging therapies for SARS-CoV-2 virus.

In European journal of pharmacology ; h5-index 57.0

The global pandemic of coronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 7,273,958 cases with almost over 413,372 deaths worldwide as per the WHO situational report 143 on COVID-19. There are no known treatment regimens with proven efficacy and vaccines thus far, posing an unprecedented challenge to identify effective drugs and vaccines for prevention and treatment. The urgency for its prevention and cure has resulted in an increased number of proposed treatment options. The high rate and volume of emerging clinical trials on therapies for COVID-19 need to be compared and evaluated to provide scientific evidence for effective medical options. Other emerging non-conventional drug discovery techniques such as bioinformatics and cheminformatics, structure-based drug design, network-based methods for prediction of drug-target interactions, artificial intelligence (AI) and machine learning (ML) and phage technique could provide alternative routes to discovering potent Anti-SARS-CoV2 drugs. While drugs are being repurposed and discovered for COVID-19, novel drug delivery systems will be paramount for efficient delivery and avoidance of possible drug resistance. This review describes the proposed drug targets for therapy, and outcomes of clinical trials that have been reported. It also identifies the adopted treatment modalities that are showing promise, and those that have failed as drug candidates. It further highlights various emerging therapies and future strategies for the treatment of COVID-19 and delivery of Anti-SARS-CoV2 drugs.

Omolo Calvin A, Soni Nikki, Fasiku Victoria Oluwaseun, Mackraj Irene, Govender Thirumala


COVID-19, Clinical trials, Drug targets, Re-purposing, SARS-CoV2, Vaccines

General General

Artificial intelligence and COVID-19: A multidisciplinary approach.

In Integrative medicine research ; h5-index 20.0

The COVID-19 pandemic is taking a colossal toll in human suffering and lives. A significant amount of new scientific research and data sharing is underway due to the pandemic which is still rapidly spreading. There is now a growing amount of coronavirus related datasets as well as published papers that must be leveraged along with artificial intelligence (AI) to fight this pandemic by driving news approaches to drug discovery, vaccine development, and public awareness. AI can be used to mine this avalanche of new data and papers to extract new insights by cross-referencing papers and searching for patterns that AI algorithms could help discover new possible treatments or help in vaccine development. Drug discovery is not a trivial task and AI technologies like deep learning can help accelerate this process by helping predict which existing drugs, or brand-new drug-like molecules could treat COVID-19. AI techniques can also help disseminate vital information across the globe and reduce the spread of false information about COVID-19. The positive power and potential of AI must be harnessed in the fight to slow the spread of COVID-19 in order to save lives and limit the economic havoc due to this horrific disease.

Ahuja Abhimanyu S, Reddy Vineet Pasam, Marques Oge


Artificial intelligence, COVID-19, Drug Discovery, Integrative medicine, Vaccine development

Public Health Public Health

The Correlation of Comorbidities on the Mortality in Patients with COVID-19: an Observational Study Based on the Korean National Health Insurance Big Data.

In Journal of Korean medical science

BACKGROUND : Mortality of coronavirus disease 2019 (COVID-19) is a major concern for quarantine departments in all countries. This is because the mortality of infectious diseases determines the basic policy stance of measures to prevent infectious diseases. Early screening of high-risk groups and taking action are the basics of disease management. This study examined the correlation of comorbidities on the mortality of patients with COVID-19.

METHODS : We constructed epidemiologic characteristics and medical history database based on the Korean National Health Insurance Service Big Data and linked COVID-19 registry data of Korea Centers for Disease Control & Prevention (KCDC) for this emergent observational cohort study. A total of 9,148 patients with confirmed COVID-19 were included. Mortalities by sex, age, district, income level and all range of comorbidities classified by International Classification of Diseases-10 based 298 categories were estimated.

RESULTS : There were 3,556 male confirmed cases, 67 deaths, and crude death rate (CDR) of 1.88%. There were 5,592 females, 63 deaths, and CDR of 1.13%. The most confirmed cases were 1,352 patients between the ages of 20 to 24, followed by 25 to 29. As a result of multivariate logistic regression analysis that adjusted epidemiologic factors to view the risk of death, the odds ratio of death would be hemorrhagic conditions and other diseases of blood and blood-forming organs 3.88-fold (95% confidence interval [CI], 1.52-9.88), heart failure 3.17-fold (95% CI, 1.88-5.34), renal failure 3.07-fold (95% CI, 1.43-6.61), prostate malignant neoplasm 2.88-fold (95% CI, 1.01-8.22), acute myocardial infarction 2.38-fold (95% CI, 1.03-5.49), diabetes was 1.82-fold (95% CI, 1.25-2.67), and other ischemic heart disease 1.71-fold (95% CI, 1.09-2.66).

CONCLUSION : We hope that this study could provide information on high risk groups for preemptive interventions. In the future, if a vaccine for COVID-19 is developed, it is expected that this study will be the basic data for recommending immunization by selecting those with chronic disease that had high risk of death, as recommended target diseases for vaccination.

Kim Dong Wook, Byeon Kyeong Hyang, Kim Jaiyong, Cho Kyu Dong, Lee Nakyoung


COVID-19, Chronic Diseases, Comorbidities, Mortality Risk

Public Health Public Health

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia.

In International journal of medical sciences

Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results. Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

Albahli Saleh


Chest diseases, Coronavirus, Deep learning, Inception-V3, ResNet-152, X-ray

Public Health Public Health

Shape-based Machine Learning Models for the potential Novel COVID-19 protease inhibitors assisted by Molecular Dynamics Simulation.

In Current topics in medicinal chemistry ; h5-index 40.0

BACKGROUND : The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases has overwhelmed health and public health services. AI and ML algorithms have extended its major role in tracking the disease patterns, and in identifying possible treatment of disease.

OBJECTIVE : To identify potential COVID-19 protease inhibitor through shape-based Machine Learning assisted by Molecular docking and Molecular Dynamics simulation.

METHODS : 31 repurposed compounds have been selected targeting coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from 3D shape to pharmacophoric features of its seed compound. Ligand-Receptor docking was performed with Optimized Potential for Liquid Simulations (OPLS3) algorithms to identify high-affinity compounds from the list of selected candidates for 6LU7. This compound was subjected to Molecular Dynamic Simulations followed by ADMET studies and other analysis.

RESULTS : Shape-based Machine learning reported Remdesivir, Valrubicin, Aprepitant, and Fulvestrant and a novel therapeutic compound as the best therapeutic agents with the highest affinity for its target protein. Among the best shape-based compounds, the novel theoretical compound was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorvEMBS'. Further, toxicity analysis showed nCorv-EMBS to be efficacious that can be qualified as a 6LU7 inhibitor in COVID-19.

CONCLUSION : An effective ACE-II, GAK, AAK1, and protease 3C blockers that can serve a novel therapeutic approach to block the binding and attachment of COVID-19 protein (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 Lung cells. The novel theoretical compound nCorv-EMBS herein proposed stands as a promising inhibitor that can be extended for entering phases of clinical trials for COVID-19 treatment.

Khandelwal Ravina, Nayarisseri Anuraj, Madhavi Maddala, Selvaraj Chandrabose, Panwar Umesh, Sharma Khushboo, Hussain Tajamul, Singh Sanjeev Kumar


COVID-19, COVID-19 protease inhibitors, Machine Learning, Molecular\nDynamics Simulation, Molecular Docking, Remdesivir, Shape-based ML, nCorv-EMBS.

Pathology Pathology

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective.

In Journal of clinical pathology

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.

Browning Lisa, Colling Richard, Rakha Emad, Rajpoot Nasir, Rittscher Jens, James Jacqueline A, Salto-Tellez Manuel, Snead David R J, Verrill Clare


computer systems, image processing, computer-assisted, pathology, surgical

General General

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

In Journal of biomolecular structure & dynamics

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.

Jaiswal Aayush, Gianchandani Neha, Singh Dilbag, Kumar Vijay, Kaur Manjit


COVID-19, classification, deep learning, deep transfer learning

Surgery Surgery

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

In The Journal of bone and joint surgery. American volume

BACKGROUND : The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America.

METHODS : We developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities.

RESULTS : Our analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions, the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte's elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively.

CONCLUSIONS : We have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections), the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America.

LEVEL OF EVIDENCE : Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Vaid Shashank, Cakan Caglar, Bhandari Mohit


General General

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

In European radiology ; h5-index 62.0

OBJECTIVES : To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

METHODS : A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.

RESULTS : Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.

CONCLUSIONS : The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.

KEY POINTS : • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

Ni Qianqian, Sun Zhi Yuan, Qi Li, Chen Wen, Yang Yi, Wang Li, Zhang Xinyuan, Yang Liu, Fang Yi, Xing Zijian, Zhou Zhen, Yu Yizhou, Lu Guang Ming, Zhang Long Jiang


COVID-19, Deep learning, Diagnosis, Multidetector computed tomography, Pneumonia

Public Health Public Health

COVID-19: A master stroke of Nature.

In AIMS public health

This article presents the status of countries affected by COVID-19 (as of mid-May 2020) and their preparedness to combat the after-effects of the pandemic. The report also provides an analysis of how human behavior may have triggered such a global pandemic and why humans need to consider living sustainably to make our future world livable for all. COVID-19 originated in the city of Wuhan, China in December 2019. As of mid-May, it has spread to 213 countries and territories worldwide. The World Health Organization has declared COVID-19 a global pandemic, with a death toll of over 300,000 to date. The U.S. is currently the most impacted country. Collaborative efforts of scientists and politicians across the world will be needed to better plan and utilize global health resources to combat this global pandemic. Machine learning-based prediction models could also help by identifying potential COVID-19-prone areas and individuals. The cause of the emergence of COVID-19 is still a matter of research; however, one consistent theme is humanity's unsustainable behavior. By sustainably interacting with nature, humans may have avoided this pandemic. If unsustainable human practices are not controlled through education, awareness, behavioral change, as well as sustainable policy creation and enforcement, there could be several such pandemics in our future.

Singh Sushant K


COVID-19, Nature, coronavirus, pandemic, public health, sustainability

Public Health Public Health

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

In BMC medical research methodology

BACKGROUND : Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.

METHODS : In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.

RESULTS : The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).

CONCLUSIONS : Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Liu Nan, Chee Marcel Lucas, Niu Chenglin, Pek Pin Pin, Siddiqui Fahad Javaid, Ansah John Pastor, Matchar David Bruce, Lam Sean Shao Wei, Abdullah Hairil Rizal, Chan Angelique, Malhotra Rahul, Graves Nicholas, Koh Mariko Siyue, Yoon Sungwon, Ho Andrew Fu Wah, Ting Daniel Shu Wei, Low Jenny Guek Hong, Ong Marcus Eng Hock


COVID-19, Coronavirus, Evidence gap map, Review, SARS-CoV-2

Radiology Radiology

Chest CT evaluation of 11 persistent asymptomatic patients with SARS-CoV-2 infection.

In Japanese journal of infectious diseases

Eleven asymptomatic carriers who received nasal or throat swab test for SARS-CoV-2 after close contacts with patients who developed symptomatic 2019 coronavirus disease (COVID-19) were enrolled in this study. The chest CT images of enrolled patients were analyzed qualitatively and quantitatively. There were 3 (27.3%) patients had normal first chest CT, two of which were under age of 15 years. Lesions in 2 (18.2%) patients involved one lobe with unifocal presence. Subpleural lesions were seen in 7 (63.6%) patients. Ground glass opacity (GGO) was the most common sign observed in 7 (63.6%) patients. Crazy-paving pattern and consolidation were detected in 2 (18.2%) and 4 (36.4%) cases, respectively. Based on deep learning quantitative analysis, volume of intrapulmonary lesions on first CT scans was 85.73±84.46 cm3. In patients with positive findings on CT images, average interval days between positive real-time reverse transcriptase polymerase chain reaction assay and peak volume on CT images were 5.1±3.1 days. In conclusion, typical CT findings can be detected in over 70% of asymptomatic SARS-CoV-2 carriers. It mainly starts as GGO along subpleural regions and bronchi, and absorbs in nearly 5 days.

Yan Shuo, Chen Hui, Xie Ru-Ming, Guan Chun-Shuang, Xue Ming, Lv Zhi-Bin, Wei Lian-Gui, Bai Yan, Chen Bu-Dong


asymptomatic, coronavirus, multidetector computed tomography, pneumonia

Ophthalmology Ophthalmology

COVID-19 pandemic from an ophthalmology point of view.

In The Indian journal of medical research

Coronavirus disease 2019 (COVID-19) is caused by a highly contagious RNA virus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ophthalmologists are at high-risk due to their proximity and short working distance at the time of slit-lamp examination. Eye care professionals can be caught unaware because conjunctivitis may be one of the first signs of COVID-19 at presentation, even precluding the emergence of additional symptoms such as dry cough and anosmia. Breath and eye shields as well as N95 masks, should be worn while examining patients with fever, breathlessness, or any history of international travel or travel from any hotspot besides maintaining hand hygiene. All elective surgeries need to be deferred. Adults or children with sudden-onset painful or painless visual loss, or sudden-onset squint, or sudden-onset floaters or severe lid oedema need a referral for urgent care. Patients should be told to discontinue contact lens wear if they have any symptoms of COVID-19. Cornea retrieval should be avoided in confirmed cases and suspects, and long-term preservation medium for storage of corneas should be encouraged. Retinal screening is unnecessary for coronavirus patients taking chloroquine or hydroxychloroquine as the probability of toxic damage to the retina is less due to short-duration of drug therapy. Tele-ophthalmology and artificial intelligence should be preferred for increasing doctor-patient interaction.

Gupta Parul Chawla, Kumar M Praveen, Ram Jagat


Chloroquine - contact lens - coronavirus - eye donation - eye shields - hydroxychloroquine - ophthalmologist

General General

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

In Journal of medical systems ; h5-index 48.0

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at ).

Brinati Davide, Campagner Andrea, Ferrari Davide, Locatelli Massimo, Banfi Giuseppe, Cabitza Federico


Blood tests, COVID-19, Machine learning, RT-PCR test, Random forest, Three-way

General General

Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

In Studies in health technology and informatics ; h5-index 23.0

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.

Sakagianni Aikaterini, Feretzakis Georgios, Kalles Dimitris, Koufopoulou Christina, Kaldis Vasileios


Artificial intelligence, AutoML Vision, COVID-19, automated machine learning, chest CT scan, coronavirus disease, image classification

General General

Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.

In Studies in health technology and informatics ; h5-index 23.0

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients' diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.

Cui Wanting, Robins Daniel, Finkelstein Joseph


Big Data Analytics, Unsupervised Machine Learning

Ophthalmology Ophthalmology

Tele-Neuro-Ophthalmology: Vision for 20/20 and Beyond.

In Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society

BACKGROUND : Telehealth provides health care to a patient from a provider at a distant location. Prior to the COVID-19 pandemic adoption of telehealth modalities was increasing slowly but steadily. During the public health emergency rapid widespread telehealth implementation has been encouraged to promote patient and provider safety and preserve access to health care.

EVIDENCE ACQUISITION : Evidence was acquired from English language Internet-searches of medical and business literature and following breaking news on the COVID-19 pandemic and responses from health care stakeholders including policy makers, payers, physicians and health care organizations, and patients. We also had extensive discussions with colleagues who are developing telehealth techniques relevant to neuro-ophthalmology.

RESULTS : Regulatory, legal, reimbursement and cultural barriers impeded the widespread adoption of telehealth prior to the COVID-19 pandemic. With the increased use of telehealth in response to the public health emergency, we are rapidly accumulating experience and an evidence base identifying opportunities and challenges related to the widespread adoption of tele-neuro-ophthalmology. One of the major challenges is the current inability to adequately perform funduscopy remotely.

CONCLUSIONS : Telehealth is an increasingly recognized means of healthcare delivery. Tele-neuro-ophthalmology adoption is necessary for the sake of our patients, the survival of our subspecialty, and the education of our trainees and students. Telehealth does not supplant but supplements and complements in-person neuro-ophthalmologic care. Innovations in digital optical fundus photography, mobile vision testing applications, artificial intelligence and principles of channel management will facilitate further adoption of tele-neuro-ophthalmology and bring the specialty to the leading edge of healthcare delivery.

Ko Melissa, Busis Neil A


General General

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays.

METHOD : In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence.

RESULTS AND CONCLUSION : Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.

Brunese Luca, Mercaldo Francesco, Reginelli Alfonso, Santone Antonella


Artificial intelligence, COVID-19, Chest, Coronavirus, Deep learning, Transfer learning

Public Health Public Health

[Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19].

In Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently.

Pu Xiaorong, Chen Kecheng, Liu Junchi, Wen Jin, Zhneng Shangwei, Li Honghao


COVID-19, artificial intelligence, biomedical engineering, diagnosis and treatment plan, machine learning, novel coronavirus

General General

Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: a Nationwide Case-Control Study.

In Journal of Korean medical science

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic is an emerging threat worldwide. It remains unclear how comorbidities affect the risk of infection and severity of COVID-19.

METHODS : This is a nationwide retrospective case-control study of 219,961 individuals, aged 18 years or older, whose medical costs for COVID-19 testing were claimed until May 15, 2020. COVID-19 diagnosis and infection severity were identified from reimbursement data using diagnosis codes and on the basis of respiratory support use, respectively. Odds ratios (ORs) were estimated using multiple logistic regression, after adjusting for age, sex, region, healthcare utilization, and insurance status.

RESULTS : The COVID-19 group (7,341 of 219,961) was young and had a high proportion of female. Overall, 13.0% (954 of 7,341) of the cases were severe. The severe COVID-19 group had older patients and a proportion of male ratio than did the non-severe group. Diabetes (odds ratio range [ORR], 1.206-1.254), osteoporosis (ORR, 1.128-1.157), rheumatoid arthritis (ORR, 1.207-1.244), substance use (ORR, 1.321-1.381), and schizophrenia (ORR, 1.614-1.721) showed significant association with COVID-19. In terms of severity, diabetes (OR, 1.247; 95% confidential interval, 1.009-1.543), hypertension (ORR, 1.245-1.317), chronic lower respiratory disease (ORR, 1.216-1.233), chronic renal failure, and end-stage renal disease (ORR, 2.052-2.178) were associated with severe COVID-19.

CONCLUSION : We identified several comorbidities associated with COVID-19. Health care workers should be more careful while diagnosing and treating COVID-19 when patients have the abovementioned comorbidities.

Ji Wonjun, Huh Kyungmin, Kang Minsun, Hong Jinwook, Bae Gi Hwan, Lee Rugyeom, Na Yewon, Choi Hyoseon, Gong Seon Yeong, Choi Yoon Hyeong, Ko Kwang Pil, Im Jeong Soo, Jung Jaehun


COVID-19, Comorbidity, Risk Factor, SARS-CoV-2, Severity

Internal Medicine Internal Medicine

Compliance of Antihypertensive Medication and Risk of Coronavirus Disease 2019: a Cohort Study Using Big Data from the Korean National Health Insurance Service.

In Journal of Korean medical science

BACKGROUND : There is a controversy whether it is safe to continue renin-angiotensin system blockers in patients with coronavirus disease 2019 (COVID-19). We analyzed big data to investigate whether angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers have any significant effect on the risk of COVID-19. Population-based cohort study was conducted based on the prescription data from nationwide health insurance records.

METHODS : We investigated the 1,374,381 residents aged ≥ 40 years living in Daegu, the epicenter of the COVID-19 outbreak, between February and March 2020. Prescriptions of antihypertensive medication during the year before the outbreak were extracted from the National Health Insurance Service registry. Medications were categorized by types and stratified by the medication possession ratios (MPRs) of antihypertensive medications after controlling for the potential confounders. The risk of COVID-19 was estimated using a difference in difference analysis.

RESULTS : Females, older individuals, low-income earners, and recently hospitalized patients had a higher risk of infection. Patients with higher MPRs of antihypertensive medications had a consistently lower risk of COVID-19 than those with lower MPRs of antihypertensive medications and non-users. Among patients who showed complete compliance, there was a significantly lower risk of COVID-19 for those prescribed angiotensin II receptor blockers (relative risk [RR], 0.751; 95% confidence interval [CI], 0.587-0.960) or calcium channel blockers (RR, 0.768; 95% CI, 0.601-0.980).

CONCLUSION : Renin-angiotensin system blockers or other antihypertensive medications do not increase the risk of COVID-19. Patients should not stop antihypertensive medications, including renin-angiotensin system blockers, because of concerns of COVID-19.

Kim Jaiyong, Kim Dong Wook, Kim Kwang Il, Kim Hong Bin, Kim Jong Hun, Lee Yong Gab, Byeon Kyeong Hyang, Cheong Hae Kwan


Antihypertensive Medication, Big Data, Cohort Study, Coronavirus Disease 2019, Risk Assessment

Public Health Public Health

AI Surveillance during Pandemics: Ethical Implementation Imperatives.

In The Hastings Center report

Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of Covid-19, and help provide care. The use of AI for surveillance purposes (such as detecting new Covid-19 cases and gathering data from healthy and ill individuals) in a pandemic raises multiple concerns ranging from privacy to discrimination to access to care. Luckily, there exist several frameworks that can help guide stakeholders, especially physicians but also AI developers and public health officials, as they navigate these treacherous shoals. While these frameworks were not explicitly designed for AI surveillance during a pandemic, they can be adapted to help address concerns regarding privacy, human rights, and due process and equality. In a time where the rapid implementation of all tools available is critical to ending a pandemic, physicians, public health officials, and technology companies should understand the criteria for the ethical implementation of AI surveillance.

Shachar Carmel, Gerke Sara, Adashi Eli Y


Covid-19, artificial intelligence, data privacy, equality, surveillance

General General

Data stream dataset of SARS-CoV-2 genome.

In Data in brief

As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical stream representation. Thus, the dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the DSR of 1557 instances of SARS-CoV-2 virus, 11540 other instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

Barbosa Raquel de M, Fernandes Marcelo A C


COVID-19, Data stream, SARS-CoV-2

Public Health Public Health

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.

In Wellcome open research

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

Carrillo-Larco Rodrigo M, Castillo-Cara Manuel


COVID-19, clustering, k-mean, pandemic, unsupervised algorithms

General General

Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence

bioRxiv Preprint

Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences. This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Nguyen, T. T.; Abdelrazek, M.; Nguyen, D. T.; Aryal, S.; Nguyen, D. T.; Khatami, A.