Mapping the Landscape of Artificial Intelligence Applications against COVID-19

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 2.5 million confirmed cases as of April 23, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, clinical, and societal applications. We also review datasets, tools, and resources needed to facilitate AI research. Finally, we discuss strategic considerations related to the operational implementation of projects, multidisciplinary partnerships, and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

[1]  Zhang Zhang,et al.  The 2019 novel coronavirus resource. , 2020, Yi chuan = Hereditas.

[2]  Aleksandra Mojsilovic,et al.  Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models , 2020, NeurIPS 2020.

[3]  Jianyi Yang,et al.  Improved protein structure prediction using predicted interresidue orientations , 2020, Proceedings of the National Academy of Sciences.

[4]  John J. Irwin,et al.  ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..

[5]  Jonathan H. Chung,et al.  Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel , 2020, Radiology.

[6]  Dianbo Liu,et al.  A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models , 2020, ArXiv.

[7]  Bayesian semiparametric time varying model for count data to study the spread of the COVID-19 cases , 2020 .

[8]  David N. Fisman,et al.  An IDEA for Short Term Outbreak Projection: Nearcasting Using the Basic Reproduction Number , 2013, PloS one.

[9]  Iztok Fister,et al.  Discovering associations in COVID-19 related research papers , 2020, ArXiv.

[10]  A. Zhavoronkov.  Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry. , 2018, Molecular pharmaceutics.

[11]  Yanjie Wei,et al.  Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov , 2020, Interdisciplinary Sciences: Computational Life Sciences.

[12]  Mauricio Santillana,et al.  Accurate estimation of influenza epidemics using Google search data via ARGO , 2015, Proceedings of the National Academy of Sciences.

[13]  Fadi Dornaika,et al.  Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19 , 2020, Journal of Medical Systems.

[14]  Pitoyo Hartono.  Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder , 2020, ArXiv.

[15]  Eric J Topol,et al.  Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. , 2020, The Lancet. Digital health.

[16]  Guo-Wei Wei,et al.  Machine intelligence design of 2019-nCoV drugs , 2020, bioRxiv.

[17]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[18]  J. Zarocostas.  How to fight an infodemic , 2020, The Lancet.

[19]  Alexandra Luccioni,et al.  Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19 , 2020, ArXiv.

[20]  Pushmeet Kohli,et al.  Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) , 2019, Proteins.

[21]  Haruki Nakamura,et al.  Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive. , 2017, Methods in molecular biology.

[22]  J. P. Donnelly,et al.  Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients , 2020, medRxiv.

[23]  Gurjit S. Randhawa,et al.  Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study , 2020, bioRxiv.

[24]  Russ B. Altman,et al.  Predicting HLA class II antigen presentation through integrated deep learning , 2019, Nature Biotechnology.

[25]  Kyriaki Kalimeri,et al.  Advertisers Jump on Coronavirus Bandwagon: Politics, News, and Business , 2020, ArXiv.

[26]  Xiaowei Xu,et al.  A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia , 2020, Engineering.

[27]  Trevor Bedford,et al.  Nextstrain: real-time tracking of pathogen evolution , 2017, bioRxiv.

[28]  Till Döhmen,et al.  DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images , 2020, ArXiv.

[29]  Andrew R. Leach,et al.  ChEMBL: towards direct deposition of bioassay data , 2018, Nucleic Acids Res..

[30]  Bo Xu,et al.  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2020, European Radiology.

[31]  Dinggang Shen,et al.  Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images , 2020, ArXiv.

[32]  Yaozong Gao,et al.  Lung Infection Quantification of COVID-19 in CT Images with Deep Learning , 2020, ArXiv.

[33]  Alessandro Vespignani,et al.  Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model , 2010, J. Comput. Sci..

[34]  Guangtao Zhai,et al.  Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner , 2020, ArXiv.

[35]  Arni S. R. Srinivasa Rao,et al.  Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine , 2020, Infection Control & Hospital Epidemiology.

[36]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[37]  K. Yuen,et al.  Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review , 2020, Radiology. Cardiothoracic imaging.

[38]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[39]  Kristina Lerman,et al.  COVID-19: The First Public Coronavirus Twitter Dataset , 2020, ArXiv.

[40]  Xian-gao Jiang,et al.  Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity , 2020 .

[41]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[42]  Robert Kirkpatrick,et al.  Artificial intelligence cooperation to support the global response to COVID-19 , 2020, Nature Machine Intelligence.

[43]  Pitoyo Hartono.  Mixing Autoencoder With Classifier: Conceptual Data Visualization , 2020, IEEE Access.

[44]  Muhammad Nabeel,et al.  AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app , 2020, Informatics in Medicine Unlocked.

[45]  Stephen E. Chick,et al.  ICU capacity management during the COVID-19 pandemic using a process simulation , 2020, Intensive Care Medicine.

[46]  Maximilian Mozes,et al.  Measuring Emotions in the COVID-19 Real World Worry Dataset , 2020, NLPCOVID19.

[47]  Li Jin,et al.  Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide , 2020, Frontiers in Artificial Intelligence.

[48]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[49]  Kristina Lerman,et al.  Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set , 2020, JMIR public health and surveillance.

[50]  Cynthia Liu,et al.  Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases , 2020, ACS central science.

[51]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Theodosia Togia,et al.  Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns , 2019, BioNLP@ACL.

[53]  Oren Etzioni,et al.  CORD-19: The Covid-19 Open Research Dataset , 2020, NLPCOVID19.

[54]  George Papadatos,et al.  ChEMBL web services: streamlining access to drug discovery data and utilities , 2015, Nucleic Acids Res..

[55]  F. Hu,et al.  Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model , 2020, 2003.00728.

[56]  Ganesh Kamath,et al.  Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking Simulations , 2020, 2004.03766.

[57]  Samarjit Kar,et al.  Neural Network Based Country Wise Risk Prediction of COVID-19 , 2020 .

[58]  Navdeep Jaitly,et al.  Adversarial Autoencoders , 2015, ArXiv.

[59]  Jiang Tao,et al.  BERE: An accurate distantly supervised biomedical entity relation extraction network , 2019, ArXiv.

[60]  Mike Preuss,et al.  Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.

[61]  Michael Feig,et al.  Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement , 2020, bioRxiv.

[62]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[63]  Andreu Vall,et al.  Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks , 2020, SSRN Electronic Journal.

[64]  Mihaela van der Schaar,et al.  AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning , 2018, ICML.

[65]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[66]  Ravi Iyengar,et al.  The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. , 2017, Cell systems.

[67]  Lian-lian Wu,et al.  Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study , 2020, medRxiv.

[68]  Kayhan Zrar Ghafoor,et al.  A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study , 2020, 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI).

[69]  Jianyang Zeng,et al.  A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 , 2020, bioRxiv.

[70]  Manuel Castillo-Cara,et al.  Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach , 2020, Wellcome open research.

[71]  Maithra Raghu,et al.  A Survey of Deep Learning for Scientific Discovery , 2020, ArXiv.

[72]  Russ B. Altman,et al.  Potential T-cell and B-cell Epitopes of 2019-nCoV , 2020, bioRxiv.

[73]  Mei U Wong,et al.  COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning , 2020, Frontiers in Immunology.

[74]  Manuel Castillo-Cara,et al.  Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. , 2020, Wellcome open research.

[75]  Sai Huang,et al.  A Novel Triage Tool of Artificial Intelligence-Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever Clinics , 2020, medRxiv.

[76]  Artem Cherkasov,et al.  Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds , 2020, Molecular informatics.

[77]  K. Cao,et al.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .

[78]  Amir Barati Farimani,et al.  Potential neutralizing antibodies discovered for novel corona virus using machine learning , 2020, Scientific Reports.

[79]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[80]  Rohan Pandey,et al.  A machine learning application for raising WASH awareness in the times of COVID-19 pandemic , 2020, Scientific reports.

[81]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[82]  G. Chowell,et al.  Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[83]  Dong Xu,et al.  AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2 , 2020, bioRxiv.

[84]  M. Cascella,et al.  Features, Evaluation and Treatment Coronavirus (COVID-19) , 2020 .

[85]  Kristen Fortney,et al.  Drug Repurposing Using Deep Embeddings of Gene Expression Profiles. , 2018, Molecular pharmaceutics.

[86]  Matteo Cinelli,et al.  The COVID-19 social media infodemic , 2020, Scientific reports.

[87]  Alexander Wong,et al.  COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images , 2020, ArXiv.

[88]  Kam Y. J. Zhang,et al.  Pharmacophore modeling: advances, limitations, and current utility in drug discovery , 2014 .

[89]  Alejandro Lopez-Rincon,et al.  Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning , 2020, Scientific Reports.

[90]  Guo-Wei Wei,et al.  Potentially highly potent drugs for 2019-nCoV , 2020, bioRxiv.

[91]  N. F. Johnson,et al.  Hate multiverse spreads malicious COVID-19 content online beyond individual platform control , 2020, 2004.00673.

[92]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[93]  G. Herrler,et al.  SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor , 2020, Cell.

[94]  Allan Tucker,et al.  Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection , 2020, ArXiv.

[95]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[96]  J. Ioannidis,et al.  Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies , 2020, BMJ.

[97]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[98]  A. Phelan,et al.  Baricitinib as potential treatment for 2019-nCoV acute respiratory disease , 2020, The Lancet.

[99]  Shayakhmetov Rim,et al.  Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches , 2020 .

[100]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[101]  Mauricio Santillana,et al.  Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet , 2018, bioRxiv.

[102]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[103]  M. Nielsen,et al.  NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data , 2017, The Journal of Immunology.

[104]  Demis Hassabis,et al.  Improved protein structure prediction using potentials from deep learning , 2020, Nature.

[105]  Eugene V. Koonin,et al.  Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses , 2020, Proceedings of the National Academy of Sciences.

[106]  Yiming Zhang,et al.  α-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19 , 2020, ArXiv.

[107]  George Barbastathis,et al.  Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China , 2020 .

[108]  M. Xiong,et al.  Artificial Intelligence Forecasting of Covid-19 in China , 2020, International Journal of Educational Excellence.

[109]  Manlio De Domenico,et al.  Assessing the risks of 'infodemics' in response to COVID-19 epidemics. , 2020, Nature human behaviour.

[110]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[111]  Sabber Ahamed,et al.  Information Mining for COVID-19 Research From a Large Volume of Scientific Literature , 2020, ArXiv.

[112]  Mohamed Medhat Gaber,et al.  Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network , 2020, Appl. Intell..

[113]  Hong Fan,et al.  Optimization Method for Forecasting Confirmed Cases of COVID-19 in China , 2020, Journal of clinical medicine.

[114]  P. Bajardi,et al.  COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.

[115]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[116]  Jingyuan Yu,et al.  Open access institutional and news media tweet dataset for COVID-19 social science research , 2020, ArXiv.

[117]  Bonggun Shin,et al.  Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction , 2019, MLHC.

[118]  Ingemar J. Cox,et al.  Tracking COVID-19 using online search , 2020, npj Digital Medicine.

[119]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[120]  Eugene V. Koonin,et al.  Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses , 2020, bioRxiv.

[121]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[122]  John S. Brownstein,et al.  Epidemiological data from the COVID-19 outbreak, real-time case information , 2020, Scientific Data.

[123]  Mauricio Santillana,et al.  Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches , 2019, Nature Communications.

[124]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[125]  Juan M. Banda,et al.  A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration , 2020, Epidemiolgia.

[126]  Emily K. Vraga,et al.  A first look at COVID-19 information and misinformation sharing on Twitter , 2020, ArXiv.

[127]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[128]  X. Qi,et al.  Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study , 2020, medRxiv.

[129]  Hayit Greenspan,et al.  Coronavirus Detection and Analysis on Chest CT with Deep Learning , 2020, ArXiv.

[130]  Dongxiao Zhu,et al.  COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray , 2020, ArXiv.

[131]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[132]  Richard D Riley,et al.  Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal , 2020 .

[133]  G. Pask,et al.  Heuristic Self-Organization in Problems of Engineering Cybernetics , 2003 .

[134]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[135]  Weiya Shi,et al.  A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients , 2020, Annals of translational medicine.

[136]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[137]  Yung-Hsiang Chen,et al.  Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China , 2020, medRxiv.

[138]  Bernhard Y Renard,et al.  Interpretable detection of novel human viruses from genome sequencing data , 2020, bioRxiv.

[139]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[140]  Nilanjan Dey,et al.  Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak , 2020, Int. J. Interact. Multim. Artif. Intell..

[141]  Li Jin,et al.  Forecasting and evaluating intervention of Covid-19 in the World , 2020, 2003.09800.

[142]  M. Luengo-Oroz,et al.  Image analysis for understanding embryo development: a bridge from microscopy to biological insights. , 2011, Current opinion in genetics & development.

[143]  A. Echenique,et al.  Chest X-Ray Findings in 636 Ambulatory Patients with COVID-19 Presenting to an Urgent Care Center: A Normal Chest X-Ray Is no Guarantee , 2020 .

[144]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[145]  Yang Zhang,et al.  Deep‐learning contact‐map guided protein structure prediction in CASP13 , 2019, Proteins.

[146]  L. Mombaerts,et al.  A machine learning-based model for survival prediction in patients with severe COVID-19 infection , 2020, medRxiv.

[147]  S. U. K. Bukhari,et al.  The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19 , 2020, medRxiv.

[148]  K. Cao,et al.  Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.

[149]  B. Beck,et al.  Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model , 2020, bioRxiv.

[150]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[151]  Hui Yang,et al.  A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories , 2020, Nat. Mach. Intell..

[152]  Hayden C. Metsky,et al.  CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design , 2020, bioRxiv.

[153]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[154]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[155]  Ato.  COVID-19 - frequently asked questions , 2020 .

[156]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[157]  Sowmya Ramaswamy Krishnan,et al.  De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence , 2020 .

[158]  Yiyue Qian,et al.  $\alpha$-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States , 2020, IEEE Journal of Biomedical and Health Informatics.

[159]  Jeremy Blackburn,et al.  "Go eat a bat, Chang!": An Early Look on the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19 , 2020, ArXiv.

[160]  Hayit Greenspan,et al.  Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis , 2020, ArXiv.

[161]  Samir Kumar Bandyopadhyay,et al.  Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release , 2020, medRxiv.

[162]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.