  In light of the COVID-19 pandemic, it is an open challenge and critical
practical problem to find a optimal way to dynamically prescribe the best
policies that balance both the governmental resources and epidemic control in
different countries and regions. To solve this multi-dimensional tradeoff of
exploitation and exploration, we formulate this technical challenge as a
contextual combinatorial bandit problem that jointly optimizes a multi-criteria
reward function. Given the historical daily cases in a region and the past
intervention plans in place, the agent should generate useful intervention
plans that policy makers can implement in real time to minimizing both the
number of daily COVID-19 cases and the stringency of the recommended
interventions. We prove this concept with simulations of multiple realistic
policy making scenarios and demonstrate a clear advantage in providing a pareto
optimal solution in the epidemic intervention problem.
