From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic

TL;DR

This paper presents an evolutionary AI approach to automatically optimize non-pharmaceutical interventions for COVID-19, enabling tailored strategies that balance health containment with economic impact.

Contribution

It introduces evolutionary surrogate-assisted prescription (ESP) for automatic NPI strategy optimization, advancing beyond predictive models to prescriptive decision-making.

Findings

Workplace and school restrictions are most impactful.

Lifting restrictions results are often unreliable.

Alternating restrictions over time can be effective.

Abstract

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.