Journal of Modern Medical

Introduction
The world faced an unprecedented health care crisis caused by the novel coronavirus, where infection control was the main health care intervention to prevent the spread of the virus [1, 2]. Using technologies such as artificial intelligence (AI) and big data is one of the ways to deal with this disease, which we can categorize its role in health care systems as prediction, diagnosis and treatment of the disease. According to reports, on December 30, 2019, the platform of Blue Dot, an artificial intelligence-based company, witnessed an “unusual pneumonia” cases of unknown cause in Wuhan, China, which warned long before it was officially recognized as the COVID-19 disease [3]. In today’s economic era, data is an essential strategic resource for countries. Big data analysis will make health services more sustainable and efficient to lead to early intervention, prevention and optimal management. By discovering correlations between data and understanding their patterns, big data technology can improve healthcare, save lives, and reduce health system costs [4]. Both AI and big data can control the huge amount of data and help health centers to report the current situation [5]. As a result, this study aims to review the use of AI and big data in management of COVID-19 pandemic.

Methods
This is a review study. First, the related articles published in 2020 were searched in PubMed, Science Direct, SID and Google Scholar databases using the keywords Coronavirus, COVID-19, artificial intelligence, and big data in Persian and English. The inclusion criteria were the availability of full text and publication in Farsi or English. The search yielded 150 articles. Duplicates were removed using Endnote software, and relevant studies were separated from unrelated ones. Finally, 9 articles were included in the study (Figure 1).
Results
In this study, it was found that there are solutions based on AI and big data to predict and diagnose COVID-19, including models of convolutional neural networks (CNNs), deep learning technology, and machine learning technology (Table 1).



Conclusion
Studies have shown that AI can help in different ways to deal with the COVID-19. This technology has been approved in predicting, diagnosis and treatment using big data. The studies showed that CT scan plays an important role in the early diagnosis of COVID-19. Deep learning method showed more than 90% accuracy in disease diagnosis, and indicated the ability of this method in analyzing CT images. For deep learning method, there is a need to collect big data in this field so that a more detailed analysis can be done. The studies showed that big data is becoming a powerful method for analyzing data and identifying patterns that can be effective in diagnosing COVID-19. Its important role in dealing with this disease starts from the first stage, i.e. prediction and diagnosis; the more quality data there is, the more accurate it will be. One of the challenges in using AI technologies is their high costs. Another challenges are the lack of standard data sets and privacy/security issues. It is recommended that a similar review study be be conducted after the end of COVID-19.

Ethical Considerations
Compliance with ethical guidelines
There were no ethical considerations to be considered in this research.

Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or notforprofit sectors.

Authors' contributions
Study design, data analysis and data interpretation, writing and final approval: The both authors; Data collection: Hamed Momeni.

Conflicts of interest
The authors declared no conflict of interests.


References

1. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng. 2021; 14:4-15. [DOI:10.1109/RBME.2020.2987975] [PMID]
2. Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Alashqar BAM. Covid-19 detection using artificial intelligence. Int J Acad Eng Res. 2020; 4(3):18-25. [Link]
3. Odor PM, Neun M, Bampoe S, Clark S, Heaton D, Hoogenboom EM, et al. Anaesthesia and COVID-19: Infection control. Br J Anaesth. 2020; 125(1):16-24. [DOI:10.1016/j.bja.2020.03.025] [PMID] [PMCID]
4. Nakajima K, Kato H, Yamashiro T, Izumi T, Takeuchi I, Nakajima H, et al. COVID-19 pneumonia: Infection control protocol inside computed tomography suites. Jpn J Radiol. 2020; 38(5):391-3. [DOI:10.1007/s11604-020-00948-y] [PMID] [PMCID]
5. Javaid M, Haleem A, Vaishya R, Bahl S, Suman R, Vaish A. Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes Metab Syndr. 2020; 14(4):419-22. [DOI:10.1016/j.dsx.2020.04.032] [PMID] [PMCID]
6. O'Dowd K, Nair KM, Forouzandeh P, Mathew S, Grant J, Moran R, et al. Face masks and respirators in the fight against the COVID-19 pandemic: A review of current materials, advances and future perspectives. Materials. 2020; 13(15):3363. [DOI:10.3390/ma13153363] [PMID] [PMCID]
7. Pal R, Sekh AA, Kar S, Prasad DK. Neural network based country wise risk prediction of COVID-19. IEEE Accsess. Computing and Artificial Intelligence. 2020; 10(18):6448. [DOI:10.3390/app10186448]
8. Bowles J. How Canadian AI start-up BlueDot spotted Coronavirus before anyone else had a clue [Internet]. 2020 [Updated 2020 March 10]. Available from: [Link]
9. Wu J, Wang J, Nicholas S, Maitland E, Fan Q. Application of big data technology for COVID-19 prevention and control in China: Lessons and recommendations. J Med Internet Res. 2020; 22(10):e21980. [DOI:10.2196/21980] [PMID] [PMCID]
10. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health. 2020; 17(9):3176.[DOI:10.3390/ijerph17093176] [PMID] [PMCID]
11. Shaw R, Kim YK, Hua J. Governance, technology and citizen behavior in pandemic: Lessons from COVID-19 in East Asia. Prog Disaster Sci. 2020; 6:100090. [DOI:10.1016/j.pdisas.2020.100090] [PMID] [PMCID]
12. Sedik A, Iliyasu AM, Abd El-Rahiem B, Abdel Samea ME, Abdel-Raheem A, Hammad M, et al. Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses. 2020; 12(7):769. [DOI:10.3390/v12070769] [PMID] [PMCID]
13. Parsons Leigh J, Fiest K, Brundin-Mather R, Plotnikoff K, Soo A, Sypes EE, et al. A national cross-sectional survey of public perceptions of the COVID-19 pandemic: Self-reported beliefs, knowledge, and behaviors. Plos One. 2020; 15(10):e0241259. [DOI:10.1371/journal.pone.0241259] [PMID] [PMCID]

14. van Maurik IS, Bakker ED, van den Buuse S, Gillissen F, van de Beek M, Lemstra E, et al. Psychosocial effects of corona measures on patients with dementia, mild cognitive impairment and subjective cognitive decline. Front Psychiatry. 2020; 11:585686. [DOI:10.3389/fpsyt.2020.585686] [PMID] [PMCID]
15. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev. 2020;14(4):337-9. [DOI:10.1016/j.dsx.2020.04.012] [PMID] [PMCID]
16. Jain R, Gupta M, Taneja S, Hemanth DJ. Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl Intell. 2021; 51(3):1690-700. [DOI:10.1007/s10489-020-01902-1] [PMID] [PMCID]
17. Wang L, Lin ZQ, Wong A. COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep. 2020; 10(1):19549. [DOI:10.1038/s41598-020-76550-z] [PMID] [PMCID]
18. Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, et al. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked. 2020; 20:100378. [DOI:10.1016/j.imu.2020.100378] [PMID] [PMCID]
19. Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med. 2020; 121:103795. [DOI:10.1016/j.compbiomed.2020.103795] [PMID] [PMCID]
20. Maghded HS, Ghafoor KZ, Sadiq AS, Curran K, Rawat DB, Rabie K. A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: Design study. Paper presented at: 2020 IEEE 21^st International Conference on Information Reuse and Integration for Data Science (IRI). 13 August 2020; Las Vegas, USA. [DOI:10.1109/IRI49571.2020.00033]
21. Garattini C, Raffle J, Aisyah DN, Sartain F, Kozlakidis Z. Big data analytics, infectious diseases and associated ethical impacts. Philos Technol. 2019; 32(1):69-85. [DOI:10.1007/s13347-017-0278-y] [PMID] [PMCID]
22. Chae S, Kwon S, Lee D. Predicting infectious disease using deep learning and big data. Int J Environ Res Public Health. 2018; 15(8):1596.[DOI:10.3390/ijerph15081596] [PMID] [PMCID]
23. Eisenstein M. Infection forecasts powered by big data. Nature. 2018; 555(7695):S2-4. [DOI:10.1038/d41586-018-02473-5] [PMID]
24. Wu B, Tian F, Zhang M, Zeng H, Zeng Y. Cloud services with big data provide a solution for monitoring and tracking sustainable development goals. Geogr Sustain. 2020; 1(1):25-32. [DOI:10.1016/j.geosus.2020.03.006]
25. Li C, Debruyne DN, Spencer J, Kapoor V, Liu LY, Zhang B, et al. High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method. BioRxiv. 2020. [Unpublished]. [DOI:10.1101/2020.03.12.988246]
26. Eden JS, Rockett R, Carter I, Rahman H, de Ligt J, Hadfield J, et al. An emergent clade of SARS-CoV-2 linked to returned travellers from Iran. Virus Evol. 2020; 6(1):veaa027. [DOI:10.1093/ve/veaa027] [PMID] [PMCID]
27. Tátrai D, Várallyay Z. COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliability. arXiv. 2020; [Unpublished]. [DOI:10.48550/arXiv.2003.14160]
28. Strzelecki A. The second worldwide wave of interest in coronavirus since the COVID-19 outbreaks in South Korea, Italy and Iran: A google trends study. Brain Behav Immun. 2020; 88:950-1. [DOI:10.1016/j.bbi.2020.04.042] [PMID] [PMCID]
29. Zhao S, Chen H. Modeling the epidemic dynamics and control of COVID-19 outbreak in China. Quant Biol. 2020; 8(1):11-9. [DOI:10.1007/s40484-020-0199-0]
30. Castorina P, Iorio A, Lanteri D. Data analysis on Coronavirus spreading by macroscopic growth laws. Int J Mod Phys C. 2020; 31(7):2050103. [DOI:10.1142/S012918312050103X]