Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
survey

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

Published: 04 October 2021 Publication History

Abstract

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 researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Supplementary Material

chen (chen.zip)
Supplemental movie, appendix, image and software files for, A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

References

[1]
T. Abel and D. McQueen. 2020. The COVID-19 pandemic calls for spatial distancing and social closeness: Not for social distancing. Int. J. Public Health 65, 3 (2020), 231–231.
[2]
P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, and A. Mohammadi. 2020. COVID-caps: A capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recogn. Lett. 138 (2020), 638–643.
[3]
M. Al, A. Ewees, and H. Fan. 2020. Optimization method for forecasting confirmed cases of COVID-19 in China. J. Clin. Med. 9, 3 (2020), 674.
[4]
Z. Allam, G. Dey, and D. Jones. 2020. Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI 1, 2 (2020), 156–165.
[5]
S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman. 1990. Basic local alignment search tool. J. Molec. Biol. 215, 3 (1990), 403–410.
[6]
K. Andersen, A. Rambaut, W. Lipkin, E. Holmes, and R. Garry. 2020. The proximal origin of SARS-CoV-2. Nature Med. 26, 4 (2020), 450–452.
[7]
M. Andreatta and M. Nielsen. 2016. Gapped sequence alignment using artificial neural networks: Application to the MHC class I system. Bioinformatics 32, 4 (2016), 511–517.
[8]
I. Apostolopoulos and T. Mpesiana. 2020. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43, 2 (2020), 635–640.
[9]
Y. Arabi, S. Murthy, and S. Webb. 2020. COVID-19: A novel coronavirus and a novel challenge for critical care. Intens. Care Med. 46, 5 (2020), 833–836.
[10]
K. Arnold, L. Bordoli, and J. Kopp. 2006. The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics 22, 2 (2006), 195–201.
[11]
M. Awad and R. Khanna. 2015. Support vector regression. In Efficient Learning Machines. Apress, 67–80.
[12]
Baidu. 2020. Real-time COVID-19 data. Retrieved from https://voice.baidu.com/act/newpneumonia.
[13]
A. Bairoch, R. Apweiler, C. Wu, and W. Barker. 2005. The universal protein resource (UniProt). Nucleic Acids Res. 33, 1 (2005), D154–D159.
[14]
B. Beck, B. Shin, Y. Choi, and S. Park. 2020. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18 (2020), 784–790.
[15]
H. Berman, K. Henrick, and H. Nakamura. 2003. Announcing the worldwide protein data bank. Nature Struct. Molec. Biol. 10, 12 (2003), 980–980.
[16]
Microsoft Bing. 2020. COVID-19 tracker). Retrieved from https://bing.com/covid.
[17]
BlueDot. 2020. An AI epidemiologist sent the first warnings of the Wuhan virus. Retrieved from https://bluedot.global.
[18]
L. Breiman. 2001. Random forests. Mach. Learn. 45, 1 (2001), 5–32.
[19]
N. Bung, S. Krishnan, G. Bulusu, and A. Roy. 2020. De novo design of new chemical entities (NCEs) for SARS-CoV-2 using artificial intelligence.
[20]
C. Audrey. 2020. COVID-19 chest X-ray dataset initiative. Retrieved from https://github.com/agchung/Figure1-COVID-chestxray-dataset.
[21]
D. Caccavo. 2020. Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. medRxiv, 2020.
[22]
Canada. 2020. Digital government response to COVID-19. Retrieved from https://www.canada.ca/en/government/system/digital-government.
[23]
I. Castiglioni, D. Ippolito, and M. Interlenghi. 2020. Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: A first experience from Lombardy, Italy. MedRxiv, 2020.
[24]
J. Chan, S. Yuan, K. Kok, and K. To. 2020. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395, 10223 (2020), 514–523.
[25]
S. Chang, N. Harding, C. Zachreson, and O. Cliff. 2020. Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Commun. 11, 1 (2020), 1–13.
[26]
T. Chebet, Y. Li, N. Sam, and Y. Liu. 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161 (2019), 272–279.
[27]
H. Chen, J. Guo, C. Wang, F. Luo, and X. Yu. 2020. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. Lancet 395, 10226 (2020), 809–815.
[28]
J. Chen, K. Li, Q. Deng, K. Li, and Y. Philip. 2019. Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Industr. Info. 99, 1 (2019), 1–12.
[29]
J. Chen, L. Wu, and J. Zhang. 2020. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci. Rep. 10, 1 (2020), 1–11.
[30]
Q. Chen, A. Allot, and Z. Lu. 2020. Keep up with the latest coronavirus research. Nature 579, 7798 (2020), 193–193.
[31]
S. Chen, J. Yang, W. Yang, and C. Wang. 2020. COVID-19 control in China during mass population movements at New Year. Lancet 395, 10226 (2020), 764–766.
[32]
S. Chen, Z. Zhang, J. Yang, J. Wang, and X. Zhai. 2020. Fangcang shelter hospitals: A novel concept for responding to public health emergencies. Lancet 395, 10232 (2020), 1305–1314.
[33]
T. Chen and C. Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16). 785–794.
[34]
W. Chen, U. Strych, P. Hotez, and M. Bottazzi. 2020. The SARS-CoV-2 vaccine pipeline: An overview Current Tropical Medicine Reports. 7, 2 (2020), 61–64.
[35]
F. Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'17). 1251–1258.
[36]
J. Cohen, P. Morrison, and L. Dao. 2020. COVID-19 image data collection: Prospective predictions are the future. Retrieved from https://github.com/ieee8023/covid-chestxray-dataset.
[37]
Joseph Paul Cohen, Lan Dao, Karsten Roth, Paul Morrison, Yoshua Bengio, Almas F. Abbasi, Beiyi Shen, Hoshmand Kochi Mahsa, Marzyeh Ghassemi, Haifang Li, et al. 2020. Predicting COVID-19 pneumonia severity on chest x-ray with deep learning. Cureus 12, 7 (2020).
[38]
Coronacases. 2020. CT images of confirmed COVID-19 cases. Mendeley Data. Retrieved from https://coronacases.org.
[39]
D. Su. 2020. Novel corona virus 2019 dataset. Retrieved from https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019- dataset.
[40]
Y. Decastro, F. Gamboa, D. Henrion, and R. Hess. 2019. Approximate optimal designs for multivariate polynomial regression. Ann. Stat. 47, 1 (2019), 127–155.
[41]
J. Degen, C. Wegscheid, A. Zaliani, and M. Rarey. 2008. On the art of compiling and using'drug-like'chemical fragment spaces. ChemMedChem: Chem. Enabl. Drug Discov. 3, 10 (2008), 1503–1507.
[42]
M. Demirci and A. Adan. 2020. Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ 8 (2020), e9369.
[43]
E. Dong, H. Du, and L. Gardner. 2020. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Diseases 20, 5 (2020), 533–534.
[44]
S. Fong, G. Li, N. Dey, and R. Crespo. 2020. Composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput. 93 (2020), 106282.
[45]
S. Fong, G. Li, N. Dey, and R. Crespo. 2020. Finding an accurate early forecasting model from small dataset: a case of 2019-ncov novel coronavirus outbreak. Int. J. Interact. Multimedia Artific. Intell. 6, 1 (2020), 132–140.
[46]
National Center for Biotechnology Information (NCBI). 2020. Genome sequencing data of SARS-CoV-2. Retrieved from https://www.ncbi.nlm.nih.gov/genbank/sars-cov-2-seqs/.
[47]
Centers for Disease Control and Prevention. 2020. Weekly influenza confirmed cases. Retrieved from https://www.cdc.gov/flu/weekly.
[48]
European Centre for Disease Prevention and Control. 2020. Geographic distribution COVID-19 cases worldwide. Retrieved from https://ecdc.europa.eu/en/publications-data.
[49]
A. Gaulton, L. Bellis, A. Bento, J. Chambers, and M. Davies. 2012. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1 (2012), D1100–D1107.
[50]
R. Ge, Y. Qi, Y. Yan, M. Tian, and Q. Gu. 2020. The role of close contacts tracking management in COVID-19 prevention: A cluster investigation in Jiaxing, China. J. Infect. 81, 1 (2020), e71–e74.
[51]
Global Health Drug Discovery Institute (GHDDI). 2020. Targeting COVID-19: GHDDI info sharing portal. Retrieved from https://ghddi-ailab.github.io/Targeting2019-nCoV.
[52]
I. Ghinai, T. McPherson, J. Hunter, and H. Kirking. 2020. First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. Lancet 395, 10230 (2020), 1137–1144.
[53]
M. Gilman, C. Liu, A. Fung, and I. Behera. 2019. Structure of the respiratory syncytial virus polymerase complex. Cell 179, 1 (2019), 193–204.
[54]
GISAID. 2020. GISAID: global initiative on sharing all influenza data. Retrieved from https://www.gisaid.org.
[55]
D. Giuliani, M. Dickson, G. Espa, and F. Santi. 2020. Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy. BMC Infect. Diseases 20, 1 (2020), 1–10.
[56]
R. Gómez, J. Wei, D. Duvenaud, and J. Hernández. 2018. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4, 2 (2018), 268–276.
[57]
GOV.UK. 2020. Coronavirus (COVID-19) cases in the UK. Retrieved from https://coronavirus.data.gov.uk/.
[58]
O. Gozes, M. Frid, and H. Greenspan. 2020. Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection and patient monitoring using deep learning CT image analysis. Retrieved from https://arXiv:2003.05037.
[59]
K. Greff, R. Srivastava, K. Jan, and B. Steunebrink. 2016. LSTM: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28, 10 (2016), 2222–2232.
[60]
A. Hassanien, L. Mahdy, K. Ezzat, and H. Elmousalami. 2020. Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. MedRxiv, 2020.
[61]
K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'16). 770–778.
[62]
X. He, E. Lau, P. Wu, X. Deng, and J. Wang. 2020. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Med. 26, 5 (2020), 672–675.
[63]
Y. He, Z. Xiang, and H. Mobley. 2010. Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. BioMed Res. Int.2010, Article 297505 (2010).
[64]
J. Hellewell, S. Abbott, A. Gimma, and N. Bosse. 2020. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health 8, 4 (2020), e488–e496.
[65]
E. Hemdan, M. Shouman, and M. Karar. 2020. Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Retrieved from https://arXiv:2003.11055.
[66]
C. Herst, S. Burkholz, J. Sidney, A. Sette, P. Harris, S. Massey, T. Brasel, E. Cunha, D. S. Rosa, and W. Chao. 2020. 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. Vaccine 38, 28 (2020), 4464–4475.
[67]
G. Hinton, S. Sabour, and N. Frosst. 2018. Matrix capsules with EM routing. In International Conference on Learning Representations. 1–8.
[68]
Hanley J. Ho, Zoe Xiaozhu Zhang, Zhilian Huang, Aung Hein Aung, Wei-Yen Lim, and Angela Chow. 2020. 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. J. Med. Internet Res. 22, 5 (2020), e19437.
[69]
S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.
[70]
M. Hoffmann, H. Kleine, S. Schroeder, and N. Krüger. 2020. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181, 2 (2020), 271–280.
[71]
M. Hofmarcher, A. Mayr, E. Rumetshofer, and P. Ruch. 2020. Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. Soc. Sci. Res. Netw. (2020). https://ssrn.com/abstract=3561442.
[72]
Jeremy Hsu. 2020. Can AI Make Bluetooth Contact Tracing Better?IEEE Spectrum. Retrieved from https://spectrum.ieee.org/the-human-os/artificial-intelligence.
[73]
F. Hu, J. Jiang, and P. Yin. 2020. Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model. Retrieved from https://arXiv:2003.00728.
[74]
Z. Hu, Q. Ge, L. Jin, and M. Xiong. 2020. Artificial intelligence forecasting of COVID-19 in China. Retrieved from https://arXiv:2002.07112.
[75]
Z. Hu, Q. Ge, S. Li, L. Jin, and M. Xiong. 2020. Evaluating the effect of public health intervention on the global-wide spread trajectory of COVID-19. Medrxiv, 2020.
[76]
C. Huang, Y. Chen, Y. Ma, and P. Kuo. 2020. Multiple-input deep convolutional neural network model for COVID-19 forecasting in China.IEEE Access 2019, 7 (2019), 74822–74834.
[77]
C. Huang, Y. Wang, X. Li, L. Ren, and J. Zhao. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 10223 (2020), 497–506.
[78]
G. Huang, Z. Liu, D. Van, and K. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'17). 4700–4708.
[79]
L. Huang, R. Han, T. Ai, P. Yu, and H. Kang. 2020. Serialquantitative chest CT assessment of COVID-19: Deep-learning approach. Radiol.: Cardiothor. Imag. 2, 2 (2020), e200075.
[80]
IEDB. 2020. Ellipro: An antibody epitope prediction tool. Retrieved from http://tools.iedb.org/ellipro.
[81]
GitHub. Inc.2020. GitHub. Retrieved from https://github.com.
[82]
Clarivate Analytics Integrity. 2020. Clarivate analytics integrity. Retrieved from https://integrity.clarivate.com/integrity/.
[83]
M. Iqbal. 2020. Active surveillance for COVID-19 through artificial intelligence using concept of real-time speech-recognition mobile application to analyse cough sound. In IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan). IEEE, 1–2.
[84]
S. Jaeger, S. Fulle, and S. Turk. 2018. Mol2vec: Unsupervised machine learning approach with chemical intuition. J. Chem. Info. Model. 58, 1 (2018), 27–35.
[85]
J. Jang. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybernet. 23, 3 (1993), 665–685.
[86]
H. Jeffrey. 1990. Chaos game representation of gene structure. Nucleic Acids Res. 18, 8 (1990), 2163–2170.
[87]
J. Jumper, K. Tunyasuvunakool, P. Kohli, and D. Hassabis. 2020. Computational predictions of protein structures associated with COVID-19. Retrieved from https://deepmind.com.
[88]
V. Jurtz, S. Paul, M. Andreatta, P. Marcatili, B. Peters, and M. Nielsen. 2017. NetMHCpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 9 (2017), 3360–3368.
[89]
O. Kadioglu, M. Saeed, H. Johannes, and T. Efferth. 2021. Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Computers in Biology and Medicine 133 (2021), 104359.
[90]
N. Kandel, S. Chungong, A. Omaar, and J. Xing. 2020. Health security capacities in the context of COVID-19 outbreak: An analysis of international health regulations annual report data from 182 countries. Lancet 395, 10229 (2020), 1047–1053.
[91]
M. Keeling, T. Hollingsworth, and J. Read. 2020. The efficacy of contact tracing for the containment of the 2019 novel coronavirus(COVID-19). J Epidemiol. Commun. Health 74, 10 (2020), 861–866.
[92]
P. Keeling. 2008. Modeling Infectious Diseases in Humans and Animals. Princeton University Press, 362.
[93]
D. Kermany, M. Goldbaum, and W. Cai. 2018. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 5 (2018), 1122–1131.
[94]
J. Kim, M. Kang, E. Park, and D. Chung. 2019. A simple and multiplex loop-mediated isothermal amplification assay for rapid detectionof SARS-CoV. BioChip J. 13, 4 (2019), 341–351.
[95]
S. Kim, P. Thiessen, E. Bolton, and J. Chen. 2016. PubChem substance and compound databases. Nucleic Acids Res. 44, D1 (2016), D1202–D1213.
[96]
S. Kissler, C. Tedijanto, M. Lipsitch, and Y. Grad. 2020. Social distancing strategies for curbing the COVID-19 epidemic. MedRxiv, 2020.
[97]
Robert A. Kleinman and Colin Merkel. 2020. Digital contact tracing for COVID-19. Can. Med. Assoc. J. 192, 24 (2020), E653–E656.
[98]
W. Kong, Y. Li, M. Peng, and D. Kong. 2020. SARS-CoV-2 detection in patients with influenza-like illness. Nature Microbiol. 5, 5 (2020), 675–678.
[99]
J. Koo, A. Cook, M. Park, Y. Sun, and H. Sun. 2020. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: A modelling study. Lancet Infect. Diseases 20, 6 (2020), 678–688.
[100]
Genia Kostka and Sabrina Habich-Sobiegalla. 2020. In Times of Crisis: Public Perceptions Towards COVID-19 Contact Tracing Apps in China, Germany, and the U.S. Soc. Sci. Res. Netw. (2020). https://ssrn.com/abstract=3693783.
[101]
A. Kozomara, M. Birgaoanu, and S. Griffiths. 2019. miRBase: From microRNA sequences to function. Nucleic Acids Res. 47, D1 (2019), D155–D162.
[102]
A. Krizhevsky, I. Sutskever, and G. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Adv. Neural Info. Process. Syst. 25 (2012), 1097–1105.
[103]
S. Lai, I. Bogoch, and N. Ruktanonchai. 2020. Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: A travel network-based modelling study. MedRxiv, 2020.
[104]
V. Lampos, S. Moura, E. Yom, and I. Cox. 2021. Tracking COVID-19 using online search. NPJ Dig. Med. 4, 1 (2021), 1–11.
[105]
Larxel. 2020. COVID-19 X-rays. Retrieved from https://www.kaggle.com/andrewmvd/convid19-X-rays.
[106]
G. Li and E. De. 2020. Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nature Rev. Drug Discov. 19, 3 (2020), 149–150.
[107]
L. Li, L. Qin, Z. Xu, and Y. Yin. 2020. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology (2020), 200905.
[108]
L. Li, Q. Zhang, X. Wang, J. Zhang, and T. Wang. 2020. Characterizing the propagation of situational information in social media during COVID-19 epidemic: A case study on weibo. IEEE Trans. Comput. Soc. Syst. 7, 2 (2020), 556–562.
[109]
S. Li, K. Song, B. Yang, Y. Gao, and X. Gao. 2020. Preliminary assessment of the COVID-19 outbreak using 3-staged model e-ISHR. J. Shanghai Jiaotong Univ. (Sci.) 25 (2020), 157–164.
[110]
Y. Li, Z. Zhu, Y. Zhou, Y. Xia, and W. Shen. 2019. Volumetric medical image segmentation: A 3D deep coarse-to-fine framework and its adversarial examples. In Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer, 69–91.
[111]
J. Liu, R. Cao, M. Xu, X. Wang, and H. Zhang. 2020. Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro. Cell Discov. 6, 1 (2020), 1–4.
[112]
P. Liu, P. Beeler, and R. Chakrabarty. 2020. COVID-19 progression timeline and effectiveness of response-to-spread interventions across the United States. MedRxiv, 2020.
[113]
T. Liu, Y. Lin, X. Wen, R. N. Jorissen, and M. Gilson. 2007. BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, 1 (2007), D198–D201.
[114]
R. Lu, X. Wu, Z. Wan, Y. Li, and L. Zuo. 2020. Development of anovel reverse transcription loop-mediated isothermal amplification method for rapid detection ofSARS-CoV-2. Virol. Sinica 35, 3 (2020), 344–347.
[115]
R. Lu, X. Zhao, J. Li, P. Niu, B. Yang, and H. Wu. 2020. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 395, 10224 (2020), 565–574.
[116]
M. Paul. 2020. Chest X-ray images (pneumonia). Retrieved from https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/m etadata.
[117]
L. Maaten and G. Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579–2605.
[118]
Airlines Magazine. 2020. International air travel association (IATA). Retrieved from https://www.iata.org/.
[119]
H. Maghdid, A. Asaad, K. Ghafoor, A. Sadiq, and M. Khan. 2020. Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. Retrieved from https://arXiv:2004.00038.
[120]
H. Maghdid, K. Ghafoor, A. Sadiq, K. Curran, and K. Rabie. 2020. A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: Design study. In IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 180–187.
[121]
M. Marini, C. Brunner, N. Chokani, and R. Abhari. 2020. Enhancing response preparedness to influenza epidemics: Agent-based study of 2050 influenza season in Switzerland. Simul. Model. Pract. Theory 103 (2020), 102091.
[122]
M. Marini, N. Chokani, and R. Abhari. 2020. COVID-19 epidemic in Switzerland: Growth prediction and containment strategy using artificial intelligence and big data. MedRxiv, 2020.
[123]
MediaCloud. 2020. MediaCloud. Retrieved from https://www.mediacloud.org/.
[124]
Metabiota. 2020. How AI is battling the coronavirus outbreak. Retrieved from https://www.metabiota.com.
[125]
H. Metsky, C. Freije, and T. Kosoko. 2020. CRISPR-based surveillance for COVID-19 using kosoko-thoroddsen, tinna-solveig f-comprehensive machine learning design. BioRxiv, 2020.
[126]
H. Mi, A. Muruganujan, and P. Thomas. 2012. PANTHER in 2013: Modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D1 (2012), D377–D386.
[127]
T. Mihara, Y. Nishimura, Y. Shimizu, H. Nishiyama, G. Yoshikawa, H. Uehara, P. Hingamp, S. Goto, and H. Ogata. 2016. Linking virus genomes with host taxonomy. Viruses 8, 3 (2016), 66.
[128]
Shervin Minaee, Rahele Kafieh, Milan Sonka, Shakib Yazdani, and Ghazaleh Jamalipour Soufi. 2020. Deep-COVID: Predicting COVID-19 from chest x-ray images using deep transfer learning. Med. Image Anal. 65 (2020), 101794.
[129]
M. Moskal, W. Beker, R. Roszak, and E. Gajewska. 2020. Suggestions for second-pass anti-COVID-19 drugs based on the artificial intelligence measures of molecular similarity, shape and pharmacophore distribution. ChemRxiv. 2020.
[130]
A. Narin, C. Kaya, and Z. Pamuk. 2020. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Retrieved from https://arXiv:2003.10849.
[131]
Radiological Society of North America. 2020. RSNA pneumonia detection challenge. Retrieved from https://www.kaggle.com/c/rsna-pneumonia-detection-challenge.
[132]
Natioanl Health Commission of the People's Republic of China. 2020. Real-time COVID-19 report. Retrieved from http://www.nhc.gov.cn/.
[133]
British Society of Thoracic Imaging. 2020. COVID-19 BSTI imaging database. Retrieved from https://www.bsti.org.uk/training-and-education/COVID-19-bsti-im aging-database.
[134]
S. Oh, W. Pedrycz, and B. Park. 2003. Polynomial neural networks architecture: Analysis and design. Comput. Electric. Eng. 29, 6 (2003), 703–725.
[135]
E. Ong, H. Wang, M. Wong, and M. Seetharaman. 2020. Vaxign-ML: Supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics 36, 10 (2020), 3185–3191.
[136]
E. Ong, M. Wong, A. Huffman, and Y. He. 2020. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front. Immunol. 11 (2020), 1581.
[137]
Il Sole 24 ore. 2020. The coronavirus datasets in Italy. Retrieved from https://lab24.ilsole24ore.com/coronavirus.
[138]
Lara Orlandic, Tomas Teijeiro, and David Atienza. 2020. The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms. Retrieevd from https://arXiv:2009.11644.
[139]
J. Ortega, M. Serrano, F. Pujol, and H. Rangel. 2020. Role of changes in SARS-CoV-2 spike protein in the interaction with the human ACE2 receptor: NN in silico analysis. EXCLI J. 19 (2020), 410.
[140]
X. Ou, Y. Liu, X. Lei, P. Li, D. Mi, L. Ren, and L. Guo. 2020. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nature Commun, 11, 1 (2020), 1–12.
[141]
X. Pan, X. Da, W. Zhou, and L. Wang. 2020. Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: A study based on single-cell transcriptome analysis. Intens. Care Med. 46, 6 (2020), 1114–1116.
[142]
The Paper. 2020. The paper news network. Retrieved from https://www.thepaper.cn.
[143]
J. Phua, L. Weng, L. Ling, M. Egi, and C. Lim. 2020. Intensive care management of coronavirus disease 2019 (COVID-19): Challenges and recommendations. Lancet Respir. Med. 8, 5 (2020), 506–517.
[144]
B. Pierce, K. Wiehe, H. Hwang, and B. Kim. 2014. ZDOCK server: Interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30, 12 (2014), 1771–1773.
[145]
M. Pourhomayoun and M. Shakibi. 2020. Predicting mortality risk in patients with COVID-19 using artificial intelligence to help medical decision-making. MedRxiv, 2020.
[146]
M. Prachar, S. Justesen, D. Steen, S. Thorgrimsen, E. Jurgons, O. Winther, and F. Bagger. 2020. COVID-19 Vaccine Candidates: Prediction and Validation of 174 SARS-CoV-2 Epitopes. BioRxiv, 2020.
[147]
K. Preuer, G. Klambauer, F. Rippmann, and S. Hochreiter. 2019. Interpretable deep learning in drug discovery. In Explainable AI. Springer, 331–345.
[148]
N. Punn, S. Sonbhadra, and S. Agarwal. 2020. COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv, 2020.
[149]
X. Qi, Z. Jiang, Q. Yu, C. Shao, and H. Zhang. 2020. Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study. MedRxiv, 2020.
[150]
X. Qian, R. Ren, Y. Wang, Y. Guo, and J. Fang. 2020. Fighting against the common enemy of COVID-19: A practice of building a community with a shared future for mankind. Infect. Diseases Poverty 9, 1 (2020), 1–6.
[151]
R. Qiao, N. Tran, B. Shan, A. Ghodsi, and M. Li. 2020. Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19. Retrieved from arXiv:2003.10650.
[152]
R. Adrian. 2020. Detecting COVID-19 in X-ray images with keras, tensorflow, and deep learning. Retrieved from https://www.pyimagesearch.com/category/medical.
[153]
R. Tawsifur. 2020. Novel corona virus 2019 dataset. Retrieved from https://www.kaggle.com/tawsifurrahman/covid19-radiography-datab ase.
[154]
Radiopaedia. 2020. Images of COVID-19 cases. Mendeley Data. Retrieved from https://radiopaedia.org.
[155]
M. Rahman, M. Hoque, M. Islam, S. Akter, A. Rubayet, M. Siddique, O. Saha, M. Rahaman, M. Sultana, and M. Hossain. 2020. 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. PeerJ 8 (2020), e9572.
[156]
S. Rahmatizadeh, S. Valizadeh, and A. Dabbagh. 2020. The role of artificial intelligence in management of critical COVID-19 patients. J. Cell. Mol. Anesthesia 5, 1 (2020), 16–22.
[157]
G. Randhawa, M. Soltysiak, H. El, and C. de Souza. 2020. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PloS One 15, 4 (2020), e0232391.
[158]
A. Rao and J. Vazquez. 2020. 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. Infect. Control Hosp. Epidemiol. 41, 7 (2020), 826–830.
[159]
N. Rapin, O. Lund, M. Bernaschi, and F. Castiglione. 2010. Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 5, 4 (2010).
[160]
R. Rizk and A. Hassanien. 2020. COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network. Retrieved from https://arXiv:2004.05960.
[161]
R. Sameni. 2020. Mathematical modeling of epidemic diseases: A case study of the COVID-19 coronavirus. Retrieved from https://arXiv:2003.11371.
[162]
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen. 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'18). 4510–4520.
[163]
K. Santosh. 2020. AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. J. Med. Syst. 44, 5 (2020), 1–5.
[164]
B. Sarkar, M. Ullah, F. Johora, and M. Taniya. 2020. The essential facts of Wuhan novel coronavirus outbreak in china and epitope-based vaccine designing against 2019-nCoV. BioRxiv, 2020.
[165]
J. Sarkar and P. Chakrabarti. 2020. A machine learning model reveals older age and delayed hospitalization as predictors of mortality in patients with COVID-19. MedRxiv, 2020.
[166]
B. Schuller, D. Schuller, K. Qian, J. Liu, H. Zheng, and X. Li. 2020. COVID-19 and computer audition: An overview on what speech and sound analysis could contribute in the SARS-CoV-2 Corona crisis. Retrieved from https://arXiv:2003.11117.
[167]
A. Senior, R. Evans, J. Jumper, and J. Kirkpatrick. 2020. Improved protein structure prediction using potentials from deep learning. Nature 577, 7792 (2020), 706–710.
[168]
A. Senior, R. Evans, J. Jumper, J. Kirkpatrick, and L. Sifre. 2019. Protein structure prediction using multiple deep neural networks CASP13. Proteins: Struct. Funct. Bioinformat. 87, 12 (2019), 1141–1148.
[169]
P. Sethy and S. Behera. 2020. Detection of coronavirus Disease (COVID-19) based on deep features. Preprints, 2020.
[170]
F. Shan, Y. Gao, J. Wang, and W. Shi. 2021. Abnormal Lung Quantification in Chest CT Images of COVID-19 Patients with Deep Learning and its Application to Severity Prediction. Medical Physics 48, 4 (2021), 1633–1645.
[171]
Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, and Dinggang Shen. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev. Biomed. Eng.14 (2020), 4–15.
[172]
F. Shi, L. Xia, F. Shan, and D. Wu. 2021. Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. arXiv, 2021.
[173]
W. Shi, X. Peng, T. Liu, and Z. Cheng. 2020. Deep learning-based quantitative computed tomography model in predicting the severity of COVID-19: A retrospective study in 196 patients. Ann Transl Med. 9, 3 (2020), 216.
[174]
B. Shin, S. Park, K. Kang, and J. Ho. 2019. Self-attention based molecule representation for predicting drug-target interaction. In Proceedings of the Machine Learning for Healthcare Conference. PMLR, 230–248.
[175]
Gitanjali R. Shinde, Asmita B. Kalamkar, Parikshit N. Mahalle, Nilanjan Dey, Jyotismita Chaki, and Aboul Ella Hassanien. 2020. Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. SN Comput. Sci. 1, 4 (2020), 1–15.
[176]
K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. Retrieved from https://arXiv:1409.1556.
[177]
SIRM. 2020. COVID-19 database. Retrieved from https://sirm.org/category/senza-categoria/COVID-19.
[178]
M. Siwiak, P. Szczesny, and M. Siwiak. 2020. 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. Soc. Sci. Res. Netw. (2020) https://ssrn.com/abstract=3562477.
[179]
M. Skalic, J. Jiménez, D. Sabbadin, and G. De. 2019. Shape-based generative modeling for de novo drug design. J. Chem. Info. Model. 59, 3 (2019), 1205–1214.
[180]
Y. Song, S. Zheng, L. Li, X. Zhang, and X. Zhang. 2020. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Transactions on Computational Biology and Bioinformatics 99, 1 (2020), 1–14.
[181]
M. Stepniewska, P. Zielenkiewicz, and P. Siedlecki. 2018. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 34, 21 (2018), 3666–3674.
[182]
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.
[183]
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, and Z. Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'16). 2818–2826.
[184]
B. Tang, F. He, D. Liu, M. Fang, Z. Wu, and D. Xu. 2020. AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. BioRxiv, 2020.
[185]
Z. Tang, W. Zhao, X. Xie, and Z. Zhong. 2021. Severity assessment of COVID-19 using CT image features and laboratory indices. Phys. Med. Biol. 66, 3 (2021), 035015.
[186]
Z. Tanoli, Z. Alam, and V. Markus. 2018. Drug target commons 2.0: A community platform for systematic analysis of drug-target interaction profiles. Database (2018), 083.
[187]
P. Teles. 2020. Predicting the evolution Of SARS-Covid-2 in Portugal using an adapted SIR model previously used in South Korea for the MERS outbreak. Retrieved from https://arXiv:2003.10047.
[188]
I. Thevarajan, T. Nguyen, M. Koutsakos, J. Druce, L. Caly, C. Sandt, X. Jia, S. Nicholson, M. Catton, and B. Cowie. 2020. Breadth of concomitant immune responses prior to patient recovery: A case report of non-severe COVID-19. Nature Med. 26, 4 (2020), 453–455.
[189]
H. Tian, Y. Liu, Y. Li, and C. Wu. 2020. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368, 6491 (2020), 638–642.
[190]
K. To, O. Tsang, W. Leung, and A. Tam. 2020. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: An observational cohort study. Lancet Infect. Diseases 20, 5 (2020), 565–574.
[191]
A. Toda. 2020. Susceptible-infected-recovered (SIR) dynamics of COVID-19 and economic impact. Retrieved from https://arXiv:2003.11221.
[192]
F. Touret and X. Lamballerie. 2020. Of chloroquine and COVID-19. Antiviral Research177 (2020), 104762.
[193]
Google Trends. 2020. Coronavirus search trends. Retrieved from https://trends.google.com/trends/story/US_cu_4Rjdh3ABAABMHM_en.
[194]
O. Trott and A. Olson. 2010. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 2 (2010), 455–461.
[195]
R. Viner, S. Russell, H. Croker, J. Packer, J. Ward, C. Stansfield, O. Mytton, and C. Bonell. 2020. School closure and management practices during coronavirus outbreaks including COVID-19: A rapid systematic review. Lancet Child Adolesc. Health 4, 5 (2020), 397–404.
[196]
R. Vita, L. Zarebski, J. Greenbaum, and H. Emami. 2010. The immune epitope database 2.0. Nucleic Acids Res. 38, suppl_1 (2010), D854–D862.
[197]
I. Wallach, M. Dzamba, and A. Heifets. 2015. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Retrieved from https://arXiv:1510.02855.
[198]
A. Walls, X. Xiong, and Y. Park. 2019. Unexpected receptor functional mimicry elucidates activation of coronavirus fusion. Cell 176, 5 (2019), 1026–1039.
[199]
A. C. Walls, Y. Park, M. Tortorici, and A. Wall. 2020. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 181, 2 (2020), 281–292.
[200]
H. Wang, Y. Zhang, S. Lu, and S. Wang. 2020. Tracking and forecasting milepost moments of the epidemic in the early-outbreak: Framework and applications to the COVID-19. F1000Res. 9 (2020).
[201]
L. Wang and A. Wong. 2020. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. Sci. Rep. 10, 1 (2020), 1–12.
[202]
M. Wang, R. Cao, L. Zhang, and X. Yang. 2020. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 30, 3 (2020), 269–271.
[203]
S. Wang, B. Kang, J. Ma, and X. Zeng. 2021. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). European Radiology31 (2021), 6096–6104.
[204]
X. Wang, Y. Peng, L. Lu, and R. Summers. 2017. Hospital-scale Chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR'17).
[205]
Y. Wang, M. Hu, Q. Li, and X. Zhang. 2020. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. Retrieved from https://arXiv:2002.05534.
[206]
D. Ward, M. Higgins, J. Phelan, M. Hibberd, S. Campino, and T. Clark. 2021. An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets. Genome Med. 13, 1 (2021), 1–12.
[207]
WHO. 2021. Novel coronavirus 2019 (COVID-19). Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019 /situation-reports.
[208]
R. Wölfel, V. Corman, W. Guggemos, and M. Seilmaier. 2020. Virological assessment of hospitalized patients with COVID-2019. Nature 581, 7809 (2020), 465–469.
[209]
Worldpop. 2020. The statistics datasets on holidays and air travel. Retrieved from https://www.worldpop.org.
[210]
F. Wu, S. Zhao, B. Yu, Y. Chen, and W. Wang. 2020. A new coronavirus associated with human respiratory disease in China. Nature 579, 7798 (2020), 265–269.
[211]
J. Wu, K. Leung, M. Bushman, N. Kishore, R. Niehus, and P. Salazar. 2020. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nature Med. 26, 4 (2020), 506–510.
[212]
J. Wu, K. Leung, and G. Leung. 2020. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. Lancet 395, 10225 (2020), 689–697.
[213]
J. Wu, P. Zhang, L. Zhang, W. Meng, and J. Li. 2020. Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results. MedRxiv, 2020.
[214]
B. Xu, B. Gutierrez, S. Mekaru, and K. Sewalk. 2020. Epidemiological data from the COVID-19 outbreak, real-time case information. Sci. Data 7, 1 (2020), 1–6.
[215]
X. Xu, X. Jiang, C. Ma, P. Du, X. Li, and S. Lv. 2020. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6, 10 (2020), 1122–1129.
[216]
Y. Xu, X. Li, B. Zhu, H. Liang, and C. Fang. 2020. Characteristics of pediatric SARS-CoV-2 infection and potential evidence for persistent fecal viral shedding. Nature Med. 26, 4 (2020), 502–505.
[217]
L. Yan, H. Zhang, J. Goncalves, and Y. Xiao. 2020. A machine learning-based model for survival prediction in patients with severe COVID-19 infection. MedRxiv, 2020.
[218]
L. Yan, H. Zhang, Y. Xiao, and M. Wang. 2020. Prediction of criticality in patients with severe COVID-19 infection using three clinical features: A machine learning-based prognostic model with clinical data in Wuhan. MedRxiv, 2020.
[219]
H. Yang, W. Xie, X. Xue, and K. Yang. 2005. Design of wide-spectrum inhibitors targeting coronavirus main proteases. PLoS Biol. 3, 10 (2005).
[220]
W. Yang, Q. Cao, L. Qin, X. Wang, and Z. Cheng. 2020. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center study in Wenzhou city, Zhejiang, China. J. Infect. 80, 4 (2020), 388–393.
[221]
X. Yang. 2012. Flower pollination algorithm for global optimization. In Proceedings of the International Conference on Unconventional Computing and Natural Computation. Springer, 240–249.
[222]
Z. Yang, Z. Zeng, and K. Wang. 2020. Modified SEIR and AI prediction of the epidemics trend of COVID-19 under public health interventions. J. Thoracic Dis. 12, 3 (2020), 165.
[223]
B. Zareie, A. Roshani, M. Mansournia, and M. Rasouli. 2020. A model for COVID-19 prediction in Iran based on China parameters. Arch. Iran. Med. 23, 4 (2020), 244–248.
[224]
C. Zavaleta. 2020. COVID-19: Review Indigenous peoples' data. Nature 580, 7802 (2020), 185.
[225]
J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia. 2020. COVID-19 screening on chest X-ray images using deep learning based anomaly detection. Retrieved from https://arXiv:2003.12338.
[226]
J. Zhang, H. Zeng, J. Gu, H. Li, L. Zheng, and Q. Zou. 2020. Progress and prospects on vaccine development against SARS-CoV-2. Vaccines 8, 2 (2020), 153.
[227]
J. Zhao, Y. Zhang, X. He, and P. Xie. 2020. Covid-CT-dataset: A CT scan dataset about COVID-19. Retrieved from https://github.com/UCSD-AI4H/COVID-CT.
[228]
A. Zhavoronkov, V. Aladinskiy, and A. Zhebrak. 2020. Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. Insilico Med. 307 (2020), E1.
[229]
C. Zheng, X. Deng, Q. Fu, Q. Zhou, and J. Feng. 2020. Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv, 2020.
[230]
P. Zhou, X. Yang, X. Wang, and B. Hu. 2020. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 7798 (2020), 270–273.
[231]
Y. Zhou, Y. Hou, J. Shen, and Y. Huang. 2020. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov. 6, 1 (2020), 1–18.
[232]
Z. Zhou, M. Siddiquee, N. Tajbakhsh, and J. Liang. 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, 3–11.
[233]
ZINC. 2020. ZINC database. Retrieved from https://zinc.docking.org.

Cited By

View all
  • (2024)The Future of Medical Robotics and AI-Assisted DiagnosticsMedical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry10.4018/979-8-3693-2105-8.ch020(325-342)Online publication date: 31-May-2024
  • (2024)A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 PandemicWSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE10.37394/23208.2024.21.2121(200-217)Online publication date: 25-Apr-2024
  • (2024)Agent-based social simulations for health crises response: utilising the everyday digital health perspectiveFrontiers in Public Health10.3389/fpubh.2023.133715111Online publication date: 17-Jan-2024
  • Show More Cited By

Index Terms

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

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 54, Issue 8
      November 2022
      754 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/3481697
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 October 2021
      Accepted: 01 May 2021
      Revised: 01 March 2021
      Received: 01 May 2020
      Published in CSUR Volume 54, Issue 8

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Artificial intelligence
      2. COVID-19
      3. SARS-CoV-2

      Qualifiers

      • Survey
      • Refereed

      Funding Sources

      • National Key R&D Program of China
      • National Natural Science Foundation of China
      • International Postdoctoral Exchange Fellowship Program
      • NSF

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1,456
      • Downloads (Last 6 weeks)20
      Reflects downloads up to 04 Oct 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)The Future of Medical Robotics and AI-Assisted DiagnosticsMedical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry10.4018/979-8-3693-2105-8.ch020(325-342)Online publication date: 31-May-2024
      • (2024)A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 PandemicWSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE10.37394/23208.2024.21.2121(200-217)Online publication date: 25-Apr-2024
      • (2024)Agent-based social simulations for health crises response: utilising the everyday digital health perspectiveFrontiers in Public Health10.3389/fpubh.2023.133715111Online publication date: 17-Jan-2024
      • (2024)Emerging applications of artificial intelligence in pathogen genomicsFrontiers in Bacteriology10.3389/fbrio.2024.13269583Online publication date: 6-Mar-2024
      • (2024)COVID-19 Modeling: A ReviewACM Computing Surveys10.1145/368615057:1(1-42)Online publication date: 7-Oct-2024
      • (2024)Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect DatasetIEEE Transactions on Industrial Informatics10.1109/TII.2024.340405320:9(11475-11484)Online publication date: Sep-2024
      • (2024)Fine-Grained Lesion Classification Framework for Early Auxiliary DiagnosisIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2023.326010821:4(971-982)Online publication date: Jul-2024
      • (2024)A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition, and TrackingIEEE Internet of Things Journal10.1109/JIOT.2023.333623211:18(29275-29286)Online publication date: 15-Sep-2024
      • (2024)LoSNet: A Tailored Deep Neural Network Framework for Precise Length of Stay Prediction in Disease-Specific HospitalizationProcedia Computer Science10.1016/j.procs.2024.04.245235(2599-2608)Online publication date: 2024
      • (2024)Approaching epidemiological dynamics of COVID-19 with physics-informed neural networksJournal of the Franklin Institute10.1016/j.jfranklin.2024.106671361:6(106671)Online publication date: Apr-2024
      • Show More Cited By

      View Options

      Get Access

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media