  In this paper we propose a data-driven model for the spread of SARS-CoV-2 and
use it to design optimal control strategies of human-mobility restrictions that
both curb the epidemic and minimize the economic costs associated with
implementing non-pharmaceutical interventions. We develop an extension of the
SEIR epidemic model that captures the effects of changes in human mobility on
the spread of the disease. The parameters of our data-driven model are learned
using a multitask learning approach that leverages both data on the number of
deaths across a set of regions, and cellphone data on individuals' mobility
patterns specific to each region. We propose an optimal control problem on this
data-driven model with a tractable solution provided by geometric programming.
The result of this framework is a mobility-based intervention strategy that
curbs the spread of the epidemic while obeying a budget on the economic cost
incurred. Furthermore, in the absence of a straightforward mapping from human
mobility data to economic costs, we propose a practical method by which a
budget on economic losses incurred may be chosen to eliminate excess deaths due
to over-utilization of hospital resources. Our results are demonstrated with
numerical simulations using real data from the Philadelphia metropolitan area.
