Abstract
COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.
Keywords: COVID-19, artificial intelligence, pandemic, pathogens, diagnosis, imaging.
Graphical Abstract
[1]
Zafar MZ, Gulzar H. A case study: Choler occupational medicine & health affairs. OMICS Int 2016; 4(6): 1-3.
[2]
Zietz BP, Dunkelberg H. The history of the plague and the research on the causative agent Yersinia pestis. Int J Hyg Environ Health 2004; 207(2): 165-78.
[http://dx.doi.org/10.1078/1438-4639-00259] [PMID: 15031959]
[http://dx.doi.org/10.1078/1438-4639-00259] [PMID: 15031959]
[3]
Ataee R, Mehrabi-Tavana , Izadi M, Hosseini SMJ, Ataee MH. Bacterial meningitis: A new risk factor. J Res Med Sci 2011; 16(2): 207-10.
[PMID: 22091233]
[PMID: 22091233]
[4]
Taubenberger JK, Morens DM. The pathology of influenza virus infections. Annu Rev Patho 2008; 3: 499-522.
[http://dx.doi.org/10.1146/annurev.pathmechdis.3.121806.154316] [PMID: 18039138]
[http://dx.doi.org/10.1146/annurev.pathmechdis.3.121806.154316] [PMID: 18039138]
[5]
Mubarak A, Alturaiki W, Hemida MG. Middle east respiratory syndrome coronavirus (MERS-CoV): Infection, immunological response, and vaccine development. J Immuno Res 2019; 2019: 6491738.
[http://dx.doi.org/10.1155/2019/6491738] [PMID: 31089478]
[http://dx.doi.org/10.1155/2019/6491738] [PMID: 31089478]
[6]
Groneberg D, Zhang L, Welte T, Zabel P, Chung KF. Severe acute respiratory syndrome: Global initiatives for disease diagnosis. QJM 2003; 96(11): 845-52.
[http://dx.doi.org/10.1093/qjmed/hcg146] [PMID: 14566040]
[http://dx.doi.org/10.1093/qjmed/hcg146] [PMID: 14566040]
[7]
Chan JFW, Lau SKP, To KKW, Cheng VCC, Woo PCY, Yuen KY. Middle East respiratory syndrome coronavirus: Another zoonotic betacoronavirus causing SARS-like disease. Clin Microbio Rev 2015; 28(2): 465-522.
[http://dx.doi.org/10.1128/CMR.00102-14] [PMID: 25810418]
[http://dx.doi.org/10.1128/CMR.00102-14] [PMID: 25810418]
[8]
Zaki M, van Boheemen S, Bestebroer TM, Osterhaus DME, Fouchier RM. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabi. N Eng J Med 2012; 367(19): 1814-20.
[http://dx.doi.org/10.1056/NEJMoa1211721] [PMID: 23075143]
[http://dx.doi.org/10.1056/NEJMoa1211721] [PMID: 23075143]
[9]
Maghdid HS, Ghafoor KZ, Sadiq S, Curran K, Rabie K. A novel AI-enabled framework to diagnose coronavirus Covid 19 using smartphone embedded sensors: Design study. Computer Science, human-computer interaction, Cornell University, eprint 2020.
[10]
Butt C, Gill J, Chun D, Babu B. Deep learning system to screen coronavirus disease 2019 pneumonia. App Intel 2020; 2020 Article 6359
[http://dx.doi.org/10.1007/s10489-020-01714-3]
[http://dx.doi.org/10.1007/s10489-020-01714-3]
[11]
Equbal A, Akhter S, Sood K, Equbal I. The usefulness of additive manufacturing (AM) in COVID-19. Annals 3D Printed Med 2021; 2: 1-8.
[12]
Linda WL, Lin ZQ, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Electrical engineering and systems science, image and video processing, Cornell University 2020.
[13]
Pakistan COVID - Coronavirus Statistics. Available at: https://www.worldometers.info/coronavirus
[14]
Nguyen TT. Artificial intelligence in the battle against coronavirus (COVID 19): A survey and future research directions. Preprint 2020. arXiv:2008.07343 [cs.CY].
[http://dx.doi.org/10.13140/RG.2.2.36491.23846]
[http://dx.doi.org/10.13140/RG.2.2.36491.23846]
[15]
Vaccines for COVID-19 | CDC. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines
[16]
Cho H, Jung YH, Cho HB, Kim HT, Kim KS. Positive control synthesis method for COVID-19 diagnosis by one-step real-time RT-PCR. Clin Chim Acta 2020; 511: 149-53.
[http://dx.doi.org/10.1016/j.cc2020.10.001] [PMID: 33058837]
[http://dx.doi.org/10.1016/j.cc2020.10.001] [PMID: 33058837]
[17]
How accurate are rapid tests for diagnosing COVID-19? Available from: https://www.cochrane.org/CD013705/INFECTN_how-accurate-are-rapid-tests-diagnosing-covid-19
[18]
Trawicki MB. Deterministic seirs epidemic model for modeling vital dynamics, vaccinations, and temporary immunity. Mathematics 2019; 5(1): 7.
[http://dx.doi.org/10.3390/math5010007]
[http://dx.doi.org/10.3390/math5010007]
[19]
Kwok KO, Tang A, Wei VWI, Park WH, Yeoh EK, Riley S. Epidemic models of contact tracing: Systematic review of transmission studies of severe acute respiratory syndrome and middle east respiratory syndrome. Comput Struct Biotechno J 2019; 17: 186-94.
[http://dx.doi.org/10.1016/j.csbj.2019.01.003] [PMID: 30809323]
[http://dx.doi.org/10.1016/j.csbj.2019.01.003] [PMID: 30809323]
[20]
Su K, Xu , Li G, et al. Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongg. Chin EBioMedicine 2019; 47: 284-92.
[http://dx.doi.org/10.1016/j.ebiom.2019.08.024] [PMID: 31477561]
[http://dx.doi.org/10.1016/j.ebiom.2019.08.024] [PMID: 31477561]
[21]
Sarker IH, Watters P, Kayes SM. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data 2019; 6(1): 1-28.
[http://dx.doi.org/10.1186/s40537-019-0219-y]
[http://dx.doi.org/10.1186/s40537-019-0219-y]
[22]
Oh Y, Park S, Ye JC. Deep learning covid-19 features on CXR using limited training data sets. IEEE Trans Med Imaging 2020; 39(8): 2688-700.
[http://dx.doi.org/10.1109/TMI.2020.2993291] [PMID: 32396075]
[http://dx.doi.org/10.1109/TMI.2020.2993291] [PMID: 32396075]
[23]
Barstugan M, Ozkaya U, Ozturk S. Coronavirus (COVID-19) classification using CT images by machine learning methods. Computer Science, Computer Vision and Pattern Recognition 2020. arXiv:2003.09424.
[24]
Li L, Qin L, Xu1a Z, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020; 2020: 200905.
[http://dx.doi.org/10.1148/radio2020200905] [PMID: 32191588]
[http://dx.doi.org/10.1148/radio2020200905] [PMID: 32191588]
[25]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 2021. Online ahead of print.
[26]
Apostolopoulos ID, Mpesiana T. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020; 43(2): 635-40.
[http://dx.doi.org/10.1007/s13246-020-00865-4] [PMID: 32524445]
[http://dx.doi.org/10.1007/s13246-020-00865-4] [PMID: 32524445]
[27]
Thanh T, Thanh DNH. Medical images denoising method based on total variation regularization and Anscombe transform. 19th International Symposium on Communications and Information Technologies. 26-30.
[http://dx.doi.org/10.1109/ISCIT.2019.8905207]
[http://dx.doi.org/10.1109/ISCIT.2019.8905207]
[28]
Prasath VBS, Thanh DNH, Hai NH. On selecting the appropriate scale in image selective smoothing by nonlinear diffusion. 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE)Hue, Vietnam. 2018.
[http://dx.doi.org/10.1109/CCE.2018.8465764]
[http://dx.doi.org/10.1109/CCE.2018.8465764]
[29]
Thanh DNH, Dvoenko S. A denoising of biomedical images. Int Arch Photogramm Remote Sens Spat Inf Sci 2015; 5: 73-8.
[http://dx.doi.org/10.5194/isprsarchives-XL-5-W6-73-2015]
[http://dx.doi.org/10.5194/isprsarchives-XL-5-W6-73-2015]
[30]
Prasath VBS, Thanh DNH, Hai NH. Regularization parameter selection in image restoration with inverse gradient: Single scale or multiscale IEEE 7th International Conference on Communications and Electronics. 278-82.
[31]
Wang Z, Bovik C, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 2004; 13(4): 600-12.
[http://dx.doi.org/10.1109/TIP.2003.819861] [PMID: 15376593]
[http://dx.doi.org/10.1109/TIP.2003.819861] [PMID: 15376593]
[32]
Thanh DNH, Prasath VBS, Son NV, Hieu M. An Adaptive Image inpainting method based on the modified mumford-shah model and multiscale parameter Estimation. Comput Opt 2019; 43(2): 251-7.
[http://dx.doi.org/10.18287/2412-6179-2019-43-2-251-257]
[http://dx.doi.org/10.18287/2412-6179-2019-43-2-251-257]
[33]
Le T, Chartrand R, Asaki TJ. A variational approach to reconstructing images corrupted by Poisson Noise. J Math Imaging Vis 2007; 27(3): 257-63.
[http://dx.doi.org/10.1007/s10851-007-0652-y]
[http://dx.doi.org/10.1007/s10851-007-0652-y]
[34]
Hai NH, Thanh DNH, Hien NN, Premachandra C, Prasath VBS. A fast denoising algorithm for x-ray images with variance stabilizing transform. International Conference on Knowledge and Systems Engineering (KSE). 1-5.
[http://dx.doi.org/10.1109/KSE.2019.8919364]
[http://dx.doi.org/10.1109/KSE.2019.8919364]
[35]
Goldstein T, Osher S. The split Bregman method for L1-regularized problems. SIAM J Imaging Sci 2009; 2(3): 323-43.
[http://dx.doi.org/10.1137/080725891]
[http://dx.doi.org/10.1137/080725891]
[36]
Thanh DNH, Kalavathi P, Thanh T, Prasath VBS. Chest X-Ray image denoising using Nesterov optimization method with total variation regularization. Procedia Comput Sci 2020; 171: 1961-9.
[http://dx.doi.org/10.1016/j.procs.2020.04.210]
[http://dx.doi.org/10.1016/j.procs.2020.04.210]
[37]
Zhang J, Xie Y, Li Y, Shen C, Xia Y. COVID-19 screening on chest x-ray images using deep learning based anomaly detection. Electrical Engineering and Systems Science, Image and Video Processing, Cornell University 2020.
[38]
Hemdan EED, Shouman M, Karar ME. COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Electrical Engineering and Systems Science, Image and Video Processing, Cornell University 2020. arXiv:2003.11055.
[39]
Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Ashqar BM. COVID-19 detection using artificial intelligence. Int J Acad Eng Res 2020; 4(3): 18-25.
[40]
Jin C, Cheny W, Caoy Y, et al. Development and evaluation of an AI system for COVID-19 diagnosis. MedRxiV 2020.
[http://dx.doi.org/10.1101/2020.03.20.20039834]
[http://dx.doi.org/10.1101/2020.03.20.20039834]
[41]
Ghoshal B, Allan Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv:2003.10769.
[42]
Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci 2021; 13(1): 103-17.
[http://dx.doi.org/10.1007/s12539-020-00403-6] [PMID: 33387306]
[http://dx.doi.org/10.1007/s12539-020-00403-6] [PMID: 33387306]
[43]
Yousefzadeh M, Esfahanian P, Movahed SMS, et al. Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans. PLoS One 2021; 16(5): e0250952.
[http://dx.doi.org/10.1371/journapone.0250952] [PMID: 33961635]
[http://dx.doi.org/10.1371/journapone.0250952] [PMID: 33961635]
[44]
Zhang K, Liu X, Shen J, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020; 181(6): 1423-33.
[http://dx.doi.org/10.1016/j.cel2020.04.045] [PMID: 32416069]
[http://dx.doi.org/10.1016/j.cel2020.04.045] [PMID: 32416069]
[45]
Jin C, Chen W, Cao Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11(1): 5088.
[http://dx.doi.org/10.1038/s41467-020-18685-1] [PMID: 33037212]
[http://dx.doi.org/10.1038/s41467-020-18685-1] [PMID: 33037212]
[46]
Randhawa GS, Soltysiak MPM, Roz HE, de Souza CPE, Hill K, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One 2020; 15(4): 1-24.
[http://dx.doi.org/10.1371/journapone.0232391]
[http://dx.doi.org/10.1371/journapone.0232391]
[47]
Tang Z, Zhao W, Xie X, et al. Severity assessment of coronavirus disease 2019 (covid-19) using quantitative features from chest CT images. Phys Med Biol 2021; 66(3): 035015.
[48]
Shereen M, Khan S, Kazmi A, Bashir N, Siddique R. COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. J Adv Res 2020; 24: 91-8.
[http://dx.doi.org/10.1016/j.jare.2020.03.005] [PMID: 32257431]
[http://dx.doi.org/10.1016/j.jare.2020.03.005] [PMID: 32257431]
[49]
Kalkreuth R, Paul Kaufmann P. COVID-19: A survey on public medical imaging data resources. arXiv:2004.04569.
[50]
Srinivasa Rao SR, Vazquez J. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect Control Hosp Epidemio 2020; 41(7): 826-30.
[http://dx.doi.org/10.1017/ice.2020.61] [PMID: 32122430]
[http://dx.doi.org/10.1017/ice.2020.61] [PMID: 32122430]
[51]
Wynants L, Calster BV, Bonten MMJ, et al. Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal. BMJ 2020; 369: m1328.
[52]
Yan L, Zhang HT, Xiao Y, et al. Prediction of survival for severe Covid-19 patients with three clinical features: Development of a machine learning-based prognostic model with clinical data in Wuhan. MedRxiV 2020.
[53]
Mohapatra S, Nath P, Chatterjee M, et al. Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking. MedRxiV 2020.
[54]
Avchaciov K, Burmistrova O, Fedichev PO. AI for the repurposing of approved or investigational drugs against COVID-19. 2020; 1-7.
[55]
Kristensen U. The science advisory board. Available from: https://www.scienceboard.net/ (accessed on May 29, 2020)
[56]
Researcher unraveling SARS-CoV-2 spike protein through music (Update). Available from: https://phys.org/news/2020-04-unraveling-sars- cov-spike-protein-music.html (accessed on 29th April, 2021)
[57]
Liu C, Zhou Q, Li Y, et al. Research and development on therapeutic agents and vaccines for COVID-19 and related human coronavirus diseases. ACS Cent Sci 2020; 6(3): 315-31.
[http://dx.doi.org/10.1021/acscentsci.0c00272] [PMID: 32226821]
[http://dx.doi.org/10.1021/acscentsci.0c00272] [PMID: 32226821]
[58]
Martineau K. Tech in 2025: 10 technologies that will transform the global economy. Available from: http://news.mit.edu/2020/mit-marshaling-artificial-intelligence-fight-against-covid-19-0519 (accessed on May 29, 2020)
[59]
Ho D. Addressing COVID-19 drug development with artificial intelligence. Adv Intell Syst 2020. Online ahead of print.
[60]
Abdulla A, Wang B, Qian F, et al. Project IDentif.AI: Harnessing artificial intelligence to rapidly optimize combination therapy development for infectious disease intervention. Adv Ther (Weinh) 2020. Online ahead of print.
[http://dx.doi.org/10.1002/adtp.202000034] [PMID: 32838027]
[http://dx.doi.org/10.1002/adtp.202000034] [PMID: 32838027]
[61]
Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front Immunol 2020; 11: 1581.
[http://dx.doi.org/10.1101/2020.03.20.000141]
[http://dx.doi.org/10.1101/2020.03.20.000141]
[62]
Ting DSW, Carin L, Dzau V, Wong TY. Digital technology and COVID-19. Nat Med 2020; 26(4): 459-61.
[http://dx.doi.org/10.1038/s41591-020-0824-5] [PMID: 32284618]
[http://dx.doi.org/10.1038/s41591-020-0824-5] [PMID: 32284618]
[63]
Nguyen DD, Guo-Wei GW. Math and AI-based repositioning of existing drugs for COVID-19. Newstest SIAM NEWS 1 2020. Available at: https://users.math.msu.edu (Accessed on May 30, 2020)
[64]
Li J, Zheng S, Chen B, Butte J, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform 2016; 17(1): 2-12.
[http://dx.doi.org/10.1093/bib/bbv020] [PMID: 25832646]
[http://dx.doi.org/10.1093/bib/bbv020] [PMID: 25832646]
[65]
Zhavoronkov A, Vladimir V, Zhebrak A, et al. Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv 2019.
[http://dx.doi.org/10.26434/chemrxiv.11829102.v2]
[http://dx.doi.org/10.26434/chemrxiv.11829102.v2]
[66]
Liu X, Wang XJ. Potential inhibitors against 2019-nCoV coronavirus M protease from clinically approved medicines. J Genet Genomics 2020; 47(2): 119-21.
[http://dx.doi.org/10.1016/j.jgg.2020.02.001] [PMID: 32173287]
[http://dx.doi.org/10.1016/j.jgg.2020.02.001] [PMID: 32173287]
[67]
Zhavoronkov A, Vanhaelen Q, Oprea TI. Will artificial intelligence for drug discovery impact clinical pharmacology? Clin Pharmaco Ther 2020; 107(4): 780-5.
[http://dx.doi.org/10.1002/cpt.1795] [PMID: 31957003]
[http://dx.doi.org/10.1002/cpt.1795] [PMID: 31957003]
[68]
Zhavoronkov A, Ivanenkov Y, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019; 37(9): 1038-40.
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]
[69]
Chenthamarakshan V, Das P, Padhi I, et al. Target-specific and selective drug design for COVID-19 using deep generative models. Neural Inform Process Syst 2020; 1-21.
[70]
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics 2020; 52(4): 200-2.
[http://dx.doi.org/10.1152/physiolgenomics.00029.2020] [PMID: 32216577]
[http://dx.doi.org/10.1152/physiolgenomics.00029.2020] [PMID: 32216577]
[71]
Pandey R, Gautam V, Jain C, et al. A machine learning application for raising wash awareness in the times of COVID-19 pandemic. arXiv:2003.07074.
[72]
McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2020; 2(4): e166-7.
[http://dx.doi.org/10.1016/S2589-7500(20)30054-6] [PMID: 32289116]
[http://dx.doi.org/10.1016/S2589-7500(20)30054-6] [PMID: 32289116]
[73]
Vaishya R, Javaid M, Khan IH, Haleem I. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr 2020; 14(4): 337-9.
[http://dx.doi.org/10.1016/j.dsx.2020.04.012] [PMID: 32305024]
[http://dx.doi.org/10.1016/j.dsx.2020.04.012] [PMID: 32305024]
[74]
Rivas V. Drones and artificial intelligence to enforce social isolation during COVID-19 outbreak. Medium towards data science 2020. Available from:https://towardsdatascience.com/drones-and-artificial-intelligence-to-enforce-social-isolation-during-covid-19-outbreak-783434b7dfa7(Accessed on 1st June, 2020)
[75]
Chun A. In a time of coronavirus, China’s investment in AI is paying off in a big way. South China Morning Post 2020. Available from:https://www.scmp.com/comment/opinion/article/3075553/time-coronavirus-chinas-investment-ai-paying-big-way(Accessed on 1st June, 2020)
[76]
Dickson B. Why AI might be the most effective weapon we have to fight COVID-19. The next web 2020. Available from:https://thenextweb.com/neural/2020/03/21/why-ai-might-be-the-most-effective-weapon-we-have-to-fight-covid-19(Accessed on 1st June, 2020)
[77]
Maslan C. Social distancing detection for COVID-19. Medium 2020. Available from:https://blog.camio.com/social-distancing-detection-for-covid-19a5c9e8d6c760?gi. (Accessed on 1st June, 2020)
[78]
OCED Using artificial intelligence to help combat COVID-19 OECD Publishing Paris 2020. Available from:http://www.oecd.org/coronavirus/policy-responses/using-artificial-intelligence-to-help-combat-covid-19-ae4c5c21(Accessed on 1st June, 2020)
[79]
Shaikh J, Devi PS, Shaikh M, Nafee K, Hailu T. Role of artificial intelligence in prevention and detection of Covid-19. Int J Adv Sci Technol 2020; 29(9): 45-54.
[80]
Naudé W. Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls. AI Soc 2020; 1-5.
[http://dx.doi.org/10.1007/s00146-020-00978-0] [PMID: 32346223]
[http://dx.doi.org/10.1007/s00146-020-00978-0] [PMID: 32346223]
[81]
Petropoulos G. Artificial intelligence in the fight against COVID-19 Bruegel 2020. Available from:https://www.bruegeorg/2020/03/artificial-intelligence-in-the-fight-against-covid-19(Accessed on 2nd June, 2020)
[82]
Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020; 579: 270-3.
[83]
Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, china, of novel coronavirus-infected pneumonia. N Eng J Med 2020; 382(13): 1199-207.
[http://dx.doi.org/10.1056/NEJMoa2001316] [PMID: 31995857]
[http://dx.doi.org/10.1056/NEJMoa2001316] [PMID: 31995857]
[84]
Ghosal S, Sengupta S, Majumder M, Sinha B. Linear regression analysis to predict the number of deaths in India due to SARS- CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020). Diabetes Metab Syndr 2020; 14(4): 311-5.
[http://dx.doi.org/10.1016/j.dsx.2020.03.017] [PMID: 32298982]
[http://dx.doi.org/10.1016/j.dsx.2020.03.017] [PMID: 32298982]
[85]
Gupta R, Pandey G, Chaudharya P, Guptab R, Palc S. SEIR and regression model based COVID-19 outbreak predictions in India. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.04.01.20049825]
[http://dx.doi.org/10.1101/2020.04.01.20049825]
[86]
Yang Z, Zeng Z, Wang K, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 2020; 12(3): 165-74.
[http://dx.doi.org/10.21037/jtd.2020.02.64] [PMID: 32274081]
[http://dx.doi.org/10.21037/jtd.2020.02.64] [PMID: 32274081]
[87]
Ardabili SF, Mosavi A, Ghamisi P, et al. COVID-19 Outbreak prediction with machine learning. Algorithms 2020; 13(10): 249.
[http://dx.doi.org/10.3390/a13100249]
[http://dx.doi.org/10.3390/a13100249]
[88]
Ndiaye BM, Tendeng L, Seck D. Analysis of the COVID-19 pandemic by SIR model and machine learning technics for fore-casting. Quantitative biology, populations and evolution, Cornell University 2020. arXiv:2004.01574.
[89]
Liu D, Clementey L, Poiriery C, 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. arXiv:2004.04019.
[90]
Ismail MM. Machine learning the phenomenology of COVID-19: From early infection dynamics. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.03.17.20037309]
[http://dx.doi.org/10.1101/2020.03.17.20037309]
[91]
Santosh KC. AI-Driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. J Med Syst 2020; 44(5): 93.
[http://dx.doi.org/10.1007/s10916-020-01562-1] [PMID: 32189081]
[http://dx.doi.org/10.1007/s10916-020-01562-1] [PMID: 32189081]
[92]
Samuel S. You-yes, you-can help AI predict the spread of coronavirus. Available from: https://www.vox.com/future-perfect/2020/3/19/21185686/ai-predicting-coronavirus-spread-forecasting-covid-19 (accessed on 21 August 2020)
Article Metrics
2