1.Shamman AH, Hadi AA, Ramul AR, Zahra MMA, Gheni HM. The artificial intelligence (AI) role for tackling against COVID-19 pandemic. Materials Today: Proceedings. 2023;80:3663-7.
2.Piccialli F, Di Cola VS, Giampaolo F, Cuomo S. The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers. 2021;23(6):1467-97.
3.Özdemir GS. The Role of Artificial Intelligence in Tackling Covid-19. World Politics in the Age of Uncertainty: The Covid-19 Pandemic, Volume 2: Springer; 2023. p. 95-108.
4.Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. Multimedia Systems. 2023;29(3):1603-27.
5.Santosh K. AI-driven tools for coronavirus outbreak: need of active learning and cross-population train/test models on multitudinal/multimodal data. Journal of medical systems. 2020;44(5):93.
6.Maghdid H, Ghafoor K, Sadiq A. A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study. arXiv. org> cs>. arXiv preprint arXiv:200307434. 2020.
7.Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology. 2020.
8.Shi Y, Wang G, Cai X-p, Deng J-w, Zheng L, Zhu H-h, et al. An overview of COVID-19. Journal of Zhejiang University Science B. 2020;21(5):343.
9.Stasi C, Fallani S, Voller F, Silvestri C. Treatment for COVID-19: An overview. European journal of pharmacology. 2020;889:173644.
10.Akpoviroro O, Sauers NK, Uwandu Q, Castagne M, Akpoviroro OP, Humayun S, et al. Severe COVID-19 infection: An institutional review and literature overview. Plos one. 2024;19(8):e0304960.
11.Kermavnar T, Visch VT, Desmet PM. Games in times of a pandemic: structured overview of COVID-19 serious games. JMIR Serious Games. 2023;11(1):e41766.
12.Kao CM. Overview of COVID-19 Infection, Treatment, and Prevention in Children. Journal of Clinical Medicine. 2024;13(2):424.
13.Kang Y, Xu S. Comprehensive overview of COVID‐19 based on current evidence. Dermatologic therapy. 2020;33(5):e13525.
14.Kaur I, Behl T, Aleya L, Rahman H, Kumar A, Arora S, et al. Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic. Environmental Science and Pollution Research. 2021;28(30):40515-32.
15.Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A. Utility of artificial intelligence amidst the COVID 19 pandemic: a review. Journal of Medical Systems. 2020;44:1-6.
16.Ahmed SF, Quadeer AA, McKay MR. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses. 2020;12(3):254.
17.Lee S-I, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature communications. 2018;9(1):42.
18.Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019;6(2):94-8.
19.Hinton G. Deep learning—a technology with the potential to transform health care. Jama. 2018;320(11):1101-2.
20.Vial A, Stirling D, Field M, Ros M, Ritz C, Carolan M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research. 2018;7(3).
21.Allen K. Chapter 13-Applications: Biosurveillance, biodefense, and biotechnology. Disaster epidemiology [Internet Academic Press, 2018: 143–51p. 2018.
22.Jalal A, Vishnuprasad V, Nishad K. Analytics, Machine Learning & NLP–use in BioSurveillance and Public Health practice. Online Journal of Public Health Informatics. 2015;7(1):e61688.
23.McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. The Lancet Digital Health. 2020;2(4):e166-e7.
24.Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic forecasting of Zika epidemics using Google Trends. PloS one. 2017;12(1):e0165085.
25.Lim S, Tucker CS, Kumara S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of biomedical informatics. 2017;66:82-94.
26.Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases. 2020;20(5):553-8.
27.Gambhir M, Bozio C, O’Hagan JJ, Uzicanin A, Johnson LE, Biggerstaff M, et al. Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clinical Infectious Diseases. 2015;60(suppl_1):S11-S9.
28.Hamzah FB, Lau C, Nazri H, Ligot DV, Lee G, Tan CL, et al. CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction. Bull World Health Organ. 2020;1(32):1-32.
29.Karako K, Song P, Chen Y, Tang W. Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Bioscience trends. 2020;14(2):134-8.
30.Akhtar M, Kraemer MU, Gardner LM. A dynamic neural network model for predicting risk of Zika in real time. BMC medicine. 2019;17:1-16.
31.Zlojutro A, Rey D, Gardner L. A decision-support framework to optimize border control for global outbreak mitigation. Scientific reports. 2019;9(1):2216.
32.Modjarrad K, Moorthy VS, Millett P, Gsell P-S, Roth C, Kieny M-P. Developing global norms for sharing data and results during public health emergencies. PLoS Medicine. 2016;13(1):e1001935.
33.Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. Neural models for predicting viral vaccine targets. Journal of bioinformatics and computational biology. 2005;3(05):1207-25.
34.Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Frontiers in immunology. 2020;11:1581.
35.Hayati M, Biller P, Colijn C. Predicting the short-term success of human influenza virus variants with machine learning. Proceedings of the Royal Society B. 2020;287(1924):20200319.
36.Rao ASS, Vazquez JA. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine. Infection Control & Hospital Epidemiology. 2020;41(7):826-30.
37.Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, et al. Web‐based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clinical Microbiology and Infection. 2014;20(1):17-21.
38.Setu A. Aarogya Setu—Apps on Google Play. play. google. com. 2020.
39.Bastawrous A, Armstrong MJ. Mobile health use in low-and high-income countries: an overview of the peer-reviewed literature. Journal of the royal society of medicine. 2013;106(4):130-42.
40.Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. Jama. 2020;323(14):1341-2.
41.Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, 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. arXiv preprint arXiv:200305037. 2020.
42.John M, Shaiba H. Main factors influencing recovery in MERS Co-V patients using machine learning. Journal of infection and public health. 2019;12(5):700-4.
43.Goh KJ, Choong MC, Cheong EH, Kalimuddin S, Wen SD, Phua GC, et al. Rapid progression to acute respiratory distress syndrome: Review of current understanding of critical illness from coronavirus disease 2019 (COVID-19) infection. Ann Acad Med Singapore. 2020;49(3):108-18.
44.Colubri A, Silver T, Fradet T, Retzepi K, Fry B, Sabeti P. Transforming clinical data into actionable prognosis models: machine-learning framework and field-deployable app to predict outcome of Ebola patients. PLoS neglected tropical diseases. 2016;10(3):e0004549.
45.Zhavoronkov A. Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. ACS Publications; 2018. p. 4311-3.
46.Anantpadma M, Lane T, Zorn KM, Lingerfelt MA, Clark AM, Freundlich JS, et al. Ebola virus Bayesian machine learning models enable new in vitro leads. ACS omega. 2019;4(1):2353-61.
47.Sarvamangala D, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence. 2022;15(1):1-22.
48.Gill SK, Christopher AF, Gupta V, Bansal P. Emerging role of bioinformatics tools and software in evolution of clinical research. Perspectives in clinical research. 2016;7(3):115-22.
49.Mbunge E, Akinnuwesi B, Fashoto SG, Metfula AS, Mashwama P. A critical review of emerging technologies for tackling COVID‐19 pandemic. Human behavior and emerging technologies. 2021;3(1):25-39.
50.Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, et al. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos, Solitons & Fractals. 2020;141:110337.
51.Quinn TP, Jacobs S, Senadeera M, Le V, Coghlan S. The three ghosts of medical AI: Can the black-box present deliver? Artificial intelligence in medicine. 2022;124:102158.
52.Marabelli M, Newell S, Handunge V. The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges. The Journal of Strategic Information Systems. 2021;30(3):101683.