  In the wake of COVID-19, every government huddles to find the best
interventions that will reduce the number of infection cases while minimizing
the economic impact. However, with many intervention policies available, how
should one decide which policy is the best course of action? In this work, we
describe an integer programming approach to prescribe intervention plans that
optimizes for both the minimal number of daily new cases and economic impact.
We present a method to estimate the impact of intervention plans on the number
of cases based on historical data. Finally, we demonstrate visualizations and
summaries of our empirical analyses on the performance of our model with
varying parameters compared to two sets of heuristics.
