  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.
