Optimizing Urban Mobility for Saving Lives and Economy During an Epidemic Outbreak, With Application to Grenoble

Abstract

This article addresses the problem of controlling human mobility in order to mitigate an epidemic in a city. We consider a discrete-time human mobility model that captures daily mobility pattern between residences and different destinations in a city and also incorporates epidemic spread at each location. For this city-wide model, we provide techniques to compute optimal mobility control policies, which tune the operating capacities of different destinations depending on their type. To obtain this kind of policies, we solve an optimization problem that takes into account the current epidemic status and maximizes the socioeconomic activity while keeping the total infections below a desired threshold. The proposed solution techniques use an outer approximation method, thanks to the monotonic nature the problem, and a receding-horizon approach. We apply these techniques to the mobility network of Grenoble metropolitan area.