A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researches to continue to maximize the advantages of AI and big data to fight COVID-19.

[1]  Genia Kostka,et al.  In Times of Crisis: Public Perceptions Towards COVID-19 Contact Tracing Apps in China, Germany and the US , 2020, SSRN Electronic Journal.

[2]  Yan Guo,et al.  Fighting against the common enemy of COVID-19: a practice of building a community with a shared future for mankind , 2020, Infectious Diseases of Poverty.

[3]  A. Askouni,et al.  Mathematical modeling of the epidemic diseases , 2020, Open Schools Journal for Open Science.

[4]  Geoffrey E. Hinton,et al.  Matrix capsules with EM routing , 2018, ICLR.

[5]  Weizhong Yang,et al.  COVID-19 control in China during mass population movements at New Year , 2020, The Lancet.

[6]  K. C. Santosh,et al.  AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data , 2020, Journal of Medical Systems.

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

[8]  Steven Y. C. Tong,et al.  Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19 , 2020, Nature Medicine.

[9]  Zhènglì Shí,et al.  Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion , 2020, Cell Research.

[10]  Yongqun He,et al.  Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens , 2020, Bioinform..

[11]  Ali Dabbagh,et al.  The role of artificial intelligence in management of critical COVID-19 patients , 2020 .

[12]  Guangdi Li,et al.  Therapeutic options for the 2019 novel coronavirus (2019-nCoV) , 2020, Nature Reviews Drug Discovery.

[13]  An effective CTL peptide vaccine for Ebola Zaire Based on Survivors’ CD8+ targeting of a particular nucleocapsid protein epitope with potential implications for COVID-19 vaccine design , 2020, Vaccine.

[14]  Yoshua Bengio,et al.  Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning , 2020, Cureus.

[15]  Balaguru Ravikumar,et al.  Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles , 2018, Database J. Biol. Databases Curation.

[16]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[17]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[18]  Haruki Nakamura,et al.  Announcing the worldwide Protein Data Bank , 2003, Nature Structural Biology.

[19]  An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets , 2021, Genome medicine.

[20]  Yiu Chung Lau,et al.  Temporal dynamics in viral shedding and transmissibility of COVID-19 , 2020, Nature Medicine.

[21]  Kang Zhang,et al.  Characteristics of pediatric SARS-CoV-2 infection and potential evidence for persistent fecal viral shedding , 2020, Nature Medicine.

[22]  P. Vollmar,et al.  Virological assessment of hospitalized patients with COVID-2019 , 2020, Nature.

[23]  Pawel Szczesny,et al.  From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates. , 2020, medRxiv.

[24]  Xiaofeng Gao,et al.  Preliminary Assessment of the COVID-19 Outbreak Using 3-Staged Model e-ISHR , 2020, Journal of Shanghai Jiaotong University (Science).

[25]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

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

[27]  Ali Mohammad Alqudah Augmented COVID-19 X-ray Images Dataset , 2020 .

[28]  Kayhan Zrar Ghafoor,et al.  Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms , 2020, Defense + Commercial Sensing.

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

[30]  Ole Winther,et al.  COVID-19 vaccine candidates: Prediction and validation of 174 novel SARS-CoV-2 epitopes , 2020 .

[31]  Antoine Geissbühler,et al.  Building a reference multimedia database for interstitial lung diseases , 2012, Comput. Medical Imaging Graph..

[32]  A. Walls,et al.  Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion , 2019, Cell.

[33]  R. Kleinman,et al.  Digital contact tracing for COVID-19 , 2020, Canadian Medical Association Journal.

[34]  Barney S. Graham,et al.  Structural Definition of a Neutralization-Sensitive Epitope on the MERS-CoV S1-NTD , 2019, Cell Reports.

[35]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

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

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

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

[40]  Diego Caccavo,et al.  Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model , 2020, medRxiv.

[41]  David Atienza,et al.  The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms , 2021, Scientific data.

[42]  Yingwei Li,et al.  Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples , 2020, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[43]  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.

[44]  Chunhua Shen,et al.  COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection , 2020, ArXiv.

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

[46]  Momiao Xiong,et al.  Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19 , 2020, medRxiv.

[47]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[48]  Shan Lu,et al.  Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 , 2020, medRxiv.

[49]  Müşerref Duygu Saçar Demirci,et al.  Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection , 2020, PeerJ.

[50]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[51]  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.

[52]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[53]  Ole Winther,et al.  COVID-19 Vaccine Candidates: Prediction and Validation of 174 SARS-CoV-2 Epitopes , 2020, bioRxiv.

[54]  E. Holmes,et al.  A new coronavirus associated with human respiratory disease in China , 2020, Nature.

[55]  Bishajit Sarkar,et al.  The Essential Facts of Wuhan Novel Coronavirus Outbreak in China and Epitope-based Vaccine Designing against COVID-19 , 2020, bioRxiv.

[56]  Jun Chen,et al.  Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography , 2020, Scientific Reports.

[57]  Dinggang Shen,et al.  Severity assessment of COVID-19 using CT image features and laboratory indices , 2020, Physics in medicine and biology.

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

[59]  R. Khanna,et al.  Support Vector Regression , 2015 .

[60]  Yongqun He,et al.  Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development , 2010, Journal of biomedicine & biotechnology.

[61]  Gourav Dey,et al.  Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally , 2020, AI.

[62]  Otun Saha,et al.  Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach , 2020, bioRxiv.

[63]  Yan Liu,et al.  Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV , 2020, Nature Communications.

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

[65]  O. Tsang,et al.  Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study , 2020, The Lancet Infectious Diseases.

[66]  F. Cheng,et al.  Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 , 2020, Cell Discovery.

[67]  Kai Zhao,et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin , 2020, Nature.

[68]  M. L. Serrano,et al.  Role of changes in SARS-CoV-2 spike protein in the interaction with the human ACE2 receptor: An in silico analysis , 2020, EXCLI journal.

[69]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[70]  Izhar Wallach,et al.  AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery , 2015, ArXiv.

[71]  D. Shen,et al.  Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction , 2020, Medical physics.

[72]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[73]  P. Klepac,et al.  Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts , 2020, The Lancet Global Health.

[74]  C. Messa,et al.  Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy , 2020, medRxiv.

[75]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

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

[77]  Gianni De Fabritiis,et al.  Shape-Based Generative Modeling for de Novo Drug Design , 2019, J. Chem. Inf. Model..

[78]  Huixia Yang,et al.  Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records , 2020, The Lancet.

[79]  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.

[80]  H. J. Jeffrey Chaos game representation of gene structure. , 1990, Nucleic acids research.

[81]  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.

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

[83]  Aboul Ella Hassanien,et al.  COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network , 2020, Studies in Computational Intelligence.

[84]  Partha Chakrabarti,et al.  A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19 , 2020, medRxiv.

[85]  Enrique Herrera-Viedma,et al.  Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction☆ , 2020, Applied Soft Computing.

[86]  M. Keeling,et al.  Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19) , 2020, Journal of Epidemiology & Community Health.

[87]  Mohammad Pourhomayoun,et al.  Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making , 2020, medRxiv.

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

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

[90]  Yingxue Li,et al.  Development of a Novel Reverse Transcription Loop-Mediated Isothermal Amplification Method for Rapid Detection of SARS-CoV-2 , 2020, Virologica Sinica.

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

[92]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[93]  Steve Webb,et al.  COVID-19: a novel coronavirus and a novel challenge for critical care , 2020, Intensive Care Medicine.

[94]  E. Holmes,et al.  The proximal origin of SARS-CoV-2 , 2020, Nature Medicine.

[95]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[96]  N. Kandel,et al.  Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries , 2020, The Lancet.

[97]  F. Hu,et al.  Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model , 2020, Biomolecules.

[98]  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.

[99]  Sonali Agarwal,et al.  COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms , 2020, medRxiv.

[100]  Rao Asrs,et al.  Identification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are under Quarantine , 2020 .

[101]  Zhiping Weng,et al.  ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers , 2014, Bioinform..

[102]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[103]  Jin Hwa Kim,et al.  A Simple and Multiplex Loop-Mediated Isothermal Amplification (LAMP) Assay for Rapid Detection of SARS-CoV , 2019, BioChip Journal.

[104]  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2021, European Radiology.

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

[106]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[108]  Susana Campino,et al.  An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets , 2020, bioRxiv.

[109]  Yanbing Ding,et al.  The epidemiology, diagnosis and treatment of COVID-19 , 2020, International Journal of Antimicrobial Agents.

[110]  Cathy H. Wu,et al.  The Universal Protein Resource (UniProt) , 2004, Nucleic Acids Res..

[111]  Xiu-Wu Pan,et al.  Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis , 2020, Intensive Care Medicine.

[112]  M. Nishimura,et al.  Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations , 2020, The Lancet Respiratory Medicine.

[113]  Zhilian Huang,et al.  Use of a Real-Time Locating System for Contact Tracing of Health Care Workers During the COVID-19 Pandemic at an Infectious Disease Center in Singapore: Validation Study , 2020, Journal of Medical Internet Research.

[114]  Ndaona Chokani,et al.  Enhancing response preparedness to influenza epidemics: Agent-based study of 2050 influenza season in Switzerland , 2020, Simul. Model. Pract. Theory.

[115]  D. Shen,et al.  Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. , 2021, Physics in medicine and biology.

[116]  Kadry Ali Ezzat,et al.  Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine , 2020, medRxiv.

[117]  Ana Kozomara,et al.  miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..

[118]  Wenjie Yang,et al.  Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China , 2020, Journal of Infection.

[119]  Mikhail Prokopenko,et al.  Modelling transmission and control of the COVID-19 pandemic in Australia , 2020, Nature communications.

[120]  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. , 2021, Annals of translational medicine.

[121]  Shuyan Li,et al.  Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results , 2020, medRxiv.

[122]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[123]  Didier Henrion,et al.  Approximate Optimal Designs for Multivariate Polynomial Regression , 2017, The Annals of Statistics.

[124]  R. Evans European Centre for Disease Prevention and Control. , 2014, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[125]  P. Xie,et al.  COVID-CT-Dataset: A CT Scan Dataset about COVID-19 , 2020, ArXiv.

[126]  Rajan K. Chakrabarty,et al.  COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States , 2020, medRxiv.

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

[128]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection: Prospective Predictions Are the Future , 2020, ArXiv.

[129]  Yuelong Shu,et al.  GISAID: Global initiative on sharing all influenza data – from vision to reality , 2017, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[130]  J. McLellan,et al.  Structure of the Respiratory Syncytial Virus Polymerase Complex , 2019, Cell.

[131]  Tao Wang,et al.  Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo , 2020, IEEE Transactions on Computational Social Systems.

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

[133]  M. Iqbal Active Surveillance for COVID-19 through artificial intelligence using concept of real-time speech-recognition mobile application to analyse cough sound. , 2020 .

[134]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

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

[136]  Li Yujian,et al.  A comparative study of fine-tuning deep learning models for plant disease identification , 2019, Comput. Electron. Agric..

[137]  Kun Qian,et al.  COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis , 2020, Frontiers in Digital Health.

[138]  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.

[139]  P. Teles,et al.  PREDICTING THE EVOLUTION OF COVID-19 IN PORTUGAL USING AN ADAPTED SIR MODEL PREVIOUSLY USED IN SOUTH KOREA FOR THE MERS OUTBREAK , 2020, medRxiv.

[140]  Farnoosh Naderkhani,et al.  COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images , 2020, Pattern Recognition Letters.

[141]  Yaozong Gao,et al.  Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification , 2021, Physics in medicine and biology.

[142]  Mei U Wong,et al.  COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning , 2020, bioRxiv.

[143]  Shan Lu,et al.  Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 , 2020, F1000Research.

[144]  Sara Szymkuć,et al.  Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution. , 2020 .

[145]  Torsten Schwede,et al.  BIOINFORMATICS Bioinformatics Advance Access published November 12, 2005 The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling , 2022 .

[146]  Massimo Bernaschi,et al.  Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System , 2010, PloS one.

[147]  Reza Sameni,et al.  Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus , 2020, 2003.11371.

[148]  Ali Ghodsi,et al.  Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19 , 2020 .

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

[150]  Nilanjan Dey,et al.  Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art , 2020, SN Computer Science.

[151]  Müşerref Duygu Saçar Demirci,et al.  Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection , 2020, bioRxiv.

[152]  Sung-Kwun Oh,et al.  Polynomial neural networks architecture: analysis and design , 2003, Comput. Electr. Eng..

[153]  A. Walls,et al.  Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein , 2020, Cell.

[154]  Till Bärnighausen,et al.  Fangcang shelter hospitals: a novel concept for responding to public health emergencies , 2020, The Lancet.

[155]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[156]  S. Lo,et al.  A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster , 2020, The Lancet.

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

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

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

[160]  Keith A. Crandall,et al.  Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach , 2020, PeerJ.

[161]  Yong Yan,et al.  The role of close contacts tracking management in COVID-19 prevention: A cluster investigation in Jiaxing, China , 2020, Journal of Infection.

[162]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[163]  Maria Elena Bottazzi,et al.  The SARS-CoV-2 Vaccine Pipeline: an Overview , 2020, Current Tropical Medicine Reports.

[164]  Dinggang Shen,et al.  Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.

[165]  E. Holmes,et al.  Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding , 2020, The Lancet.

[166]  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..

[167]  Wu Zhong,et al.  Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro , 2020, Cell Research.

[168]  Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19. , 2020, F1000Research.

[169]  Bonggun Shin,et al.  Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model , 2020, Computational and Structural Biotechnology Journal.

[170]  K. Yuen,et al.  Correction: Design of Wide-Spectrum Inhibitors Targeting Coronavirus Main Proteases , 2005, PLoS Biology.

[171]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[172]  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.

[173]  Ezz El-Din Hemdan,et al.  COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images , 2020, ArXiv.

[174]  Matthias Rarey,et al.  On the Art of Compiling and Using 'Drug‐Like' Chemical Fragment Spaces , 2008, ChemMedChem.

[175]  Sharareh R Niakan Kalhori,et al.  Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study , 2020, JMIR public health and surveillance.

[176]  De-Guang Kong,et al.  SARS-CoV-2 detection in patients with influenza-like illness , 2020, Nature Microbiology.

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

[178]  Sabrina Jaeger,et al.  Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition , 2018, J. Chem. Inf. Model..

[179]  Haoyang Sun,et al.  Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study , 2020, The Lancet Infectious Diseases.

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

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

[182]  Morten Nielsen,et al.  Gapped sequence alignment using artificial neural networks: application to the MHC class I system , 2016, Bioinform..

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

[184]  Alexis Akira Toda Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact , 2020, 2003.11221.

[185]  Gebräuchliche Fertigarzneimittel,et al.  V , 1893, Therapielexikon Neurologie.

[186]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[187]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[188]  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.

[189]  Ping-Huan Kuo,et al.  Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting , 2019, IEEE Access.

[190]  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.

[191]  Marcelo Moock,et al.  COVID-19 pandemic , 2020, Revista Brasileira de terapia intensiva.

[192]  Maria Michela Dickson,et al.  Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy , 2020, SSRN Electronic Journal.

[193]  Jiang Gu,et al.  Progress and Prospects on Vaccine Development against SARS-CoV-2 , 2020, Vaccines.

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

[195]  Wu Zhong,et al.  Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro , 2020, Cell Discovery.

[196]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[197]  G. Barlow,et al.  Lessons for managing high-consequence infections from first COVID-19 cases in the UK , 2020, The Lancet.

[198]  Marta M. Stepniewska-Dziubinska,et al.  Development and evaluation of a deep learning model for protein–ligand binding affinity prediction , 2017, Bioinform..

[199]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[200]  Jie Zhou,et al.  Development and Evaluation of an AI System for COVID-19 , 2020, medRxiv.

[201]  F. Cheng,et al.  Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 , 2020, Cell Discovery.

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

[203]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[204]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[205]  Thomas Abel,et al.  The COVID-19 pandemic calls for spatial distancing and social closeness: not for social distancing! , 2020, International Journal of Public Health.

[206]  Alessandro Sette,et al.  The Immune Epitope Database 2.0 , 2009, Nucleic Acids Res..

[207]  Friedrich Rippmann,et al.  Interpretable Deep Learning in Drug Discovery , 2019, Explainable AI.

[208]  X. de Lamballerie,et al.  Of chloroquine and COVID-19 , 2020, Antiviral Research.

[209]  R. Viner,et al.  School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review , 2020, The Lancet Child & Adolescent Health.

[210]  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).

[211]  Q. Tao,et al.  Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach , 2020, Radiology. Cardiothoracic imaging.

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

[213]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[214]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[215]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[216]  Yi Li,et al.  Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection , 2020 .

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

[218]  A. Tatem,et al.  Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study , 2020, medRxiv.

[219]  Prabira Kumar Sethy,et al.  Detection of Coronavirus Disease (COVID-19) Based on Deep Features , 2020 .

[220]  Anushya Muruganujan,et al.  PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees , 2012, Nucleic Acids Res..

[221]  Weidong Wu,et al.  Virology, Epidemiology, Pathogenesis, and Control of COVID-19 , 2020, Viruses.

[222]  Amin Roshani,et al.  A Model for COVID-19 Prediction in Iran Based on China Parameters. , 2020, Archives of Iranian medicine.

[223]  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.

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

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

[226]  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 .

[227]  Reza S. Abhari,et al.  COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data , 2020, medRxiv.

[228]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.

[229]  Philip S. Yu,et al.  Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing , 2019, IEEE Transactions on Industrial Informatics.

[230]  Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning , 2021, Computers in Biology and Medicine.

[231]  Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China , 2020, Nature Medicine.

[232]  Yonatan H. Grad,et al.  Social distancing strategies for curbing the COVID-19 epidemic , 2020, medRxiv.

[233]  H. Chandler Database , 1985 .

[234]  Susumu Goto,et al.  Linking Virus Genomes with Host Taxonomy , 2016, Viruses.

[235]  H. Kirking,et al.  First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA , 2020, The Lancet.

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