  Governments across the world are currently facing the task of selecting
suitable intervention strategies to cope with the effects of the COVID-19
pandemic. This is a highly challenging task, since harsh measures may result in
economic collapse while a relaxed strategy might lead to a high death toll.
Motivated by this, we consider the problem of forming intervention strategies
to mitigate the impact of the COVID-19 pandemic that optimize the trade-off
between the number of deceases and the socio-economic costs. We demonstrate
that the healthcare capacity and the testing rate highly affect the optimal
intervention strategies. Moreover, we propose an approach that enables
practical strategies, with a small number of policies and policy changes, that
are close to optimal. In particular, we provide tools to decide which policies
should be implemented and when should a government change to a different
policy. Finally, we consider how the presented results are affected by
uncertainty in the initial reproduction number and infection fatality rate and
demonstrate that parametric uncertainty has a more substantial effect when
stricter strategies are adopted.
