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

TL;DR

This paper develops a deep learning-based framework to optimize social distancing policies for COVID-19 by integrating economic and public health data, aiming to balance infection control with economic impact.

Contribution

It introduces a stochastic control model using mobility data and market indices to determine efficient social distancing policies with a deep learning solution.

Findings

The framework effectively models COVID-19 spread and economic impact.

Empirical results suggest optimized policies can reduce infection and economic costs.

Recommendations for US policy based on data-driven analysis.

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.