Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations
Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/162666 |
| _version_ | 1826125365107490816 |
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| author | Chen, Kexin Pun, Chi Seng Wong, Hoi Ying |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Chen, Kexin Pun, Chi Seng Wong, Hoi Ying |
| author_sort | Chen, Kexin |
| collection | NTU |
| description | Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy. |
| first_indexed | 2024-10-01T06:35:25Z |
| format | Journal Article |
| id | ntu-10356/162666 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:35:25Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1626662022-11-02T06:26:02Z Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations Chen, Kexin Pun, Chi Seng Wong, Hoi Ying School of Physical and Mathematical Sciences Science::Mathematics Google Mobility Indices Economic Modeling Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy. H.Y. Wong acknowledges the Research Matching Grant (RMG project code: 8601495) received from the Research Grants Council of Hong Kong. 2022-11-02T06:26:02Z 2022-11-02T06:26:02Z 2023 Journal Article Chen, K., Pun, C. S. & Wong, H. Y. (2023). Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations. European Journal of Operational Research, 304(1), 84-98. https://dx.doi.org/10.1016/j.ejor.2021.11.012 0377-2217 https://hdl.handle.net/10356/162666 10.1016/j.ejor.2021.11.012 34785855 2-s2.0-85120717496 1 304 84 98 en European Journal of Operational Research © 2021 Elsevier B.V. All rights reserved. |
| spellingShingle | Science::Mathematics Google Mobility Indices Economic Modeling Chen, Kexin Pun, Chi Seng Wong, Hoi Ying Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title | Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title_full | Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title_fullStr | Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title_full_unstemmed | Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title_short | Efficient social distancing during the COVID-19 pandemic: integrating economic and public health considerations |
| title_sort | efficient social distancing during the covid 19 pandemic integrating economic and public health considerations |
| topic | Science::Mathematics Google Mobility Indices Economic Modeling |
| url | https://hdl.handle.net/10356/162666 |
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