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 can 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 andevaluate 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. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest 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.

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To Appear In IEEE Transactions on Evolutionary Computation, Vol. 25 (2021), pp. 386-401.

@article{miikkulainen:ieeetec21, title={From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic}, author={Risto Miikkulainen and Olivier Francon and Elliot Meyerson and Xin Qiu and Darren Sargent and Elisa Canzani and Babak Hodjat}, volume={25}, journal={IEEE Transactions on Evolutionary Computation}, month={ }, pages={386-401}, url="http://www.cs.utexas.edu/users/ai-lab?miikkulainen:ieeetec21", year={2021} }