New research results show how to compute optimal intervention strategies
The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. All over Europe, governments discuss and decide about far-reaching counter-measures like shutdowns of economic activity and public life.
These decisions are taken facing conflicting objectives: some objectives, like the minimization of disease-related deaths, demand for strong counter-measures, while others, such as social and economic costs, require less restrictive interventions. Finding the optimal compromise boils down to solving a multi-objective optimization problem. We demonstrate how to find a mathematical description of this problem based on real-world data, and how to solve it numerically. The solution consists of a set of several optimal strategies, from which political decisions makers should select. The theory is complemented by application to counter-measures against covid-19 spreading in Berlin.
Reference:
Hanna Wulkow, Tim Conrad, Natasa Djurdjevac Conrad, Sebastian A. Mueller, Kai Nagel, and Christof Schuette (2020) Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts, available via medXrev https://medrxiv.org/cgi/content/short/2020.12.01.20241885v1
