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Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
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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.
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Coronavirus Detection and Analysis on Chest CT with Deep LearningPreprint
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Big Data Analytics, Infectious Diseases and Associated Ethical Impacts
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Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR ModelPreprint
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Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecastingPreprint
Ndiaye, B. M., Tendeng, L., & Seck, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01574 · Cited by 67
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An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in ItalyPreprint
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Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics
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Improving epidemic surveillance and response: big data is dead, long live big data
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Data analysis on Coronavirus spreading by macroscopic growth laws
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Space-time dependence of corona virus (COVID-19) outbreakPreprint
Biswas, K., & Sen, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03149 · Cited by 56
Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep modelPreprint
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Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural NetworksPreprint
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Neural Network aided quarantine control model estimation of global Covid-19 spreadPreprint
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Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.Preprint
Chaganti, S., Balachandran, A., Chabin, G., Cohen, S. L., Flohr, T. G., Georgescu, B., Greniér, P., Grbić, S., Liu, S., Mellot, F., et al. (2020) · PubMed · Cited by 48
A Time-dependent SIR model for COVID-19Preprint
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Visual Data Analysis and Simulation Prediction for COVID-19
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COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliabilityPreprint
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COVID-19: A Survey on Public Medical Imaging Data ResourcesPreprint
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Forecasting and evaluating intervention of Covid-19 in the WorldPreprint
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An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in ItalyPreprint
Perone, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00382 · Cited by 21
Fast and flexible design of novel proteins using graph neural networksPreprint
Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez-Riba, A., & Kim, P. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/868935 · Cited by 18
Visual Data Analysis and Simulation Prediction for COVID-19Preprint
Chen, B., Shi, M., Ni, X., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07096 · Cited by 17
Long, Y., Zhai, Z.-M., Han, L., Kang, J., Li, Y., Lin, Z., Zeng, L., Wu, D., Hao, C.-Q., Tang, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12028 · Cited by 16
Metropolitan-scale COVID-19 outbreaks: how similar are they?Preprint
Heroy, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01248 · Cited by 13
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Modelling death rates due to COVID-19: A Bayesian approachPreprint
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Flagged first, then by citations
33 of the 182 references couldn’t be matched to a known paper.
Research works printed but not linked: 19
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- 10.1109/ACCESS.2020.2971576 · DOI · Paper page
- 2020, arXiv Announces New COVID-19 Quick Search
- 2020, White House Announces New Partnership to Unleash U S Supercomputing Resources to Fight COVID-19
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- 2020, Seoul Introduces the COVID-19 AI Monitoring Call System
- 2020, How Next-Generation Information Technologies Tackled COVID-19 in China
- 10.2139/ssrn.3551338 · DOI
- 2020, How DAMO Academy’s AI System Detects Coronavirus Cases
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- rees, 2020, AI and Cloud Computing Used to Develop COVID-19 Vaccine
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- 2020, COVID-19 Open Research Dataset Challenge (CORD-19) An AI Challenge With AI2 CZI MSR Georgetown NIH & the White House
- 2020, IBM Releases Novel AI-Powered Technologies to Help Health and Research Community Accelerate the Discovery of Medical Insights and Treatments for COVID-19
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Cited by
Sorted by citations
Federated learning for smart healthcare: A survey
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827
Self-citationSpatial analysis and GIS in the study of COVID-19. A review
Franch-Pardo, I., Napoletano, B. M., Vergés, F. A. R., & Billa, L. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.140033 · Cited by 669
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Chengoden, R., Victor, N., Huynh‐The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R. (2023) · IEEE Access · doi:10.1109/access.2023.3241628 · Cited by 569
Self-citationCOVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S. K., & Jo, O. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00357 · Cited by 536
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Bhattacharya, S., Maddikunta, P. K. R., Pham, Q., Gadekallu, T. R., Siva Rama Krishnan, S., Chowdhary, C. L., Alazab, M., & Piran, M. J. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102589 · Cited by 495
Self-citation
Metadata
- Authors
- Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N.
- Affiliations
- Pusan National University (KR)
- Deakin University (AU)
- Corresponding author
- Hwang, Won-Joo
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 130820-130839
- ISSN
- 2169-3536 (ISSN-L)
- PMC
- PMC8545324
- OpenAlex
- W3019283372
- Cited by
- 413
- References
- 182
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 41 · 2023: 72 · 2022: 84 · 2021: 125 · 2020: 56
- FWCI
- 41.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation of Korea
- Ministry of Science and ICT (MSIT), Korea Government (NRF-2019R1C1C1006143, NRF-2019R1I1A3A01060518)
- Institute of Information & Communications Technology Planning & Evaluation
- Korea Government (MSIT) through the Artificial Intelligence Convergence Research Center, Pusan National University (2020-0-01450)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- COVID-19 pandemic, artificial intelligence applications, big data, pandemic response, epidemic control, public health communication, epidemic forecasting
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