Efficient Social Distancing for COVID-19: An Integration of Economic Health and Public Health

20 Pages Posted: 7 Dec 2020

See all articles by Kexin Chen

Kexin Chen

The Chinese University of Hong Kong (CUHK) - Department of Statistics

Chi Seng Pun

Nanyang Technological University (NTU) - School of Physical and Mathematical Sciences

Hoi Ying Wong

The Chinese University of Hong Kong (CUHK) - Department of Statistics

Date Written: December 4, 2020

Abstract

Social distancing has been the only effective way to contain the spread of an infectious disease prior to the availability of the pharmaceutical treatment. It can lower the infection rate of the disease at the economic cost. A pandemic crisis like COVID-19, however, has posed a dilemma to the policymakers since a long-term restrictive social distancing or even lockdown will keep economic cost rising. This paper investigates an efficient social distancing policy to manage the integrated risk from economic health and public health issues for COVID-19 using a stochastic epidemic modeling with mobility controls. The social distancing is to restrict the community mobility, which was recently accessible with big data analytics. This paper takes advantage of the community mobility data to model the COVID-19 processes and infer the COVID-19 driven economic values from major market index price, which allow us to formulate the search of the efficient social distancing policy as a stochastic control problem. We propose to solve the problem with a deep-learning approach. By applying our framework to the US data, we empirically examine the efficiency of the US social distancing policy and offer recommendations generated from the algorithm.

Keywords: OR in health services, COVID-19, Pandemic, Stochastic SIRD Model, Google Mobility Indices, Stochastic Controls, Deep Learning

Suggested Citation

Chen, Kexin and Pun, Chi Seng and Wong, Hoi Ying, Efficient Social Distancing for COVID-19: An Integration of Economic Health and Public Health (December 4, 2020). Available at SSRN: https://ssrn.com/abstract=3742507 or http://dx.doi.org/10.2139/ssrn.3742507

Kexin Chen (Contact Author)

The Chinese University of Hong Kong (CUHK) - Department of Statistics ( email )

Shatin, N.T.
Hong Kong

Chi Seng Pun

Nanyang Technological University (NTU) - School of Physical and Mathematical Sciences ( email )

SPMS-MAS-05-22
21 Nanyang Link
Singapore, 637371
Singapore
(+65) 6513 7468 (Phone)

HOME PAGE: http://personal.ntu.edu.sg/cspun/

Hoi Ying Wong

The Chinese University of Hong Kong (CUHK) - Department of Statistics ( email )

Shatin, N.T.
Hong Kong

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