» Articles » PMID: 34812339

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Overview
Journal IEEE Access
Date 2021 Nov 23
PMID 34812339
Citations 92
Authors
Affiliations
Soon will be listed here.
Abstract

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Citing Articles

A bibliometric analysis of the advance of artificial intelligence in medicine.

Lin M, Lin L, Lin L, Lin Z, Yan X Front Med (Lausanne). 2025; 12:1504428.

PMID: 40061376 PMC: 11885233. DOI: 10.3389/fmed.2025.1504428.


Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging.

Kumar R, Pan C, Lin Y, Yow-Ling S, Chung T, Janesha U Diagnostics (Basel). 2025; 15(3).

PMID: 39941178 PMC: 11817112. DOI: 10.3390/diagnostics15030248.


Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.

Fallatah D, Adekola H Infect Prev Pract. 2024; 6(3):100382.

PMID: 39091623 PMC: 11292357. DOI: 10.1016/j.infpip.2024.100382.


Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.

Ashique S, Mishra N, Mohanto S, Garg A, Taghizadeh-Hesary F, Gowda B Heliyon. 2024; 10(4):e25754.

PMID: 38370192 PMC: 10869876. DOI: 10.1016/j.heliyon.2024.e25754.


IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic.

Almujally N, Aljrees T, Umer M, Saidani O, Hanif D, Abuzinadah N PeerJ Comput Sci. 2023; 9:e1493.

PMID: 38077551 PMC: 10702750. DOI: 10.7717/peerj-cs.1493.


References
1.
Fomsgaard A, Rosenstierne M . An alternative workflow for molecular detection of SARS-CoV-2 - escape from the NA extraction kit-shortage, Copenhagen, Denmark, March 2020. Euro Surveill. 2020; 25(14). PMC: 7160440. DOI: 10.2807/1560-7917.ES.2020.25.14.2000398. View

2.
Srinivasa Rao A, 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 Epidemiol. 2020; 41(7):826-830. PMC: 7200852. DOI: 10.1017/ice.2020.61. View

3.
Panwar H, Gupta P, Siddiqui M, Morales-Menendez R, Singh V . Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals. 2020; 138:109944. PMC: 7254021. DOI: 10.1016/j.chaos.2020.109944. View

4.
Garattini C, Raffle J, Aisyah D, Sartain F, Kozlakidis Z . Big Data Analytics, Infectious Diseases and Associated Ethical Impacts. Philos Technol. 2019; 32(1):69-85. PMC: 6451937. DOI: 10.1007/s13347-017-0278-y. View

5.
Ozturk T, Talo M, Yildirim E, Baloglu U, Yildirim O, Rajendra Acharya U . Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020; 121:103792. PMC: 7187882. DOI: 10.1016/j.compbiomed.2020.103792. View