  Several models have been developed to predict how the COVID-19 pandemic
spreads, and how it could be contained with non-pharmaceutical interventions
(NPIs) such as social distancing restrictions and school and business closures.
This paper demonstrates how evolutionary AI could be used to facilitate the
next step, i.e. determining most effective intervention strategies
automatically. Through evolutionary surrogate-assisted prescription (ESP), it
is possible to generate a large number of candidate strategies and evaluate
them with predictive models. In principle, strategies can be customized for
different countries and locales, and balance the need to contain the pandemic
and the need to minimize their economic impact. While still limited by
available data, early experiments suggest that workplace and school
restrictions are the most important and need to be designed carefully. It also
demonstrates that results of lifting restrictions can be unreliable, and
suggests creative ways in which restrictions can be implemented softly, e.g. by
alternating them over time. As more data becomes available, the approach can be
increasingly useful in dealing with COVID-19 as well as possible future
pandemics.
