- Title
- Data-driven decision-making in COVID-19 response : a survey
- Creator
- Yu, Shuo; Qing, Qing; Zhang, Chen; Shehzad, Ahsan; Oatley, Giles; Xia, Feng
- Date
- 2021
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193902
- Identifier
- vital:18292
- Identifier
-
https://doi.org/10.1109/TCSS.2021.3075955
- Identifier
- ISSN:2329-924X (ISSN)
- Abstract
- COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Computational Social Systems Vol. 8, no. 4 (2021), p. 989-1002
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Rights
- Open Access
- Subject
- MD Multidisciplinary; COVID-19; Data-driven decision-making; Emergency response; Prevention and control
- Full Text
- Reviewed
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