  The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an
unprecedented spread and continue to cause an increasing number of fatalities
worldwide. While vaccines are still under development, social distancing,
extensive testing, and quarantining of confirmed infected subjects remain the
most effective measures to contain the pandemic. These measures carry a
significant socioeconomic cost. In this work, we introduce a novel
optimization-based decision-making framework for managing the COVID-19 outbreak
in the US. This includes modeling the dynamics of affected populations,
estimating the model parameters and hidden states from data, and an optimal
control strategy for sequencing social distancing and testing events such that
the number of infections is minimized. The analysis of our extensive
computational efforts reveals that social distancing and quarantining are most
effective when implemented early, with quarantining of confirmed infected
subjects having a much higher impact. Further, we find that "on-off" policies
alternating between strict social distancing and relaxing such restrictions can
be effective at "flattening" the curve while likely minimizing social and
economic cost.
