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http://hdl.handle.net/10397/93885
Title: | Efficient social distancing during the COVID-19 pandemic : integrating economic and public health considerations | Authors: | Chen, K Pun, CS Wong, HY |
Issue Date: | Jan-2023 | Source: | European journal of operational research, 1 Jan. 2023, v. 304, no. 1, p. 84-98 | Abstract: | 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. | Keywords: | Deep learning Economic modeling Google mobility indices OR in health services Stochastic controls Stochastic SIRD model |
Publisher: | Elsevier | Journal: | European journal of operational research | ISSN: | 0377-2217 | EISSN: | 1872-6860 | DOI: | 10.1016/j.ejor.2021.11.012 |
Appears in Collections: | Journal/Magazine Article |
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