New research, published in PLOS One, shows how optimal intervention strategies can be computed
Governments around the world are discussing and deciding on far-reaching countermeasures against the Corona pandemic. Strict restrictions on economic activity and public life are controversially discussed and implemented.
Decisions are being made with conflicting objectives: Some goals, such as minimizing disease-related deaths, require strong countermeasures, while others, such as social and economic costs, require less restrictive interventions. Finding the optimal tradeoff amounts to solving what is called a multi-objective optimization problem. We show how to find a mathematical description of this problem based on the available data on COVID-19 and how to solve it numerically. The solution consists of a set of optimal strategies from which policy makers should choose. The theory is complemented by application to countermeasures against the spread of COVID-19 in Berlin.
The related article will soon be published in PLOS One, it results from the collaboration of researchers from ZIB and TU in the project MODUS-COVID, see https://www.zib.de/projects/mobilitaetsmodelle-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, to appear in PLOS One, also available via medXrev https://medrxiv.org/cgi/content/short/2020.12.01.20241885v1
