Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic

TL;DR

This paper presents a mathematical model integrating mobility data and epidemiological dynamics to optimize lockdown strategies for COVID-19, validated with datasets from England and France.

Contribution

It introduces a novel approach combining mobile phone data with a COVID-19 spread model to determine optimal lockdown policies using model predictive control.

Findings

Robust exit strategies can be computed with realistic mobility reductions.

The methodology is validated with datasets from England and France.

Code for the experiments is publicly available.

Abstract

A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making, and we exemplify the applicability of the methodology using datasets from England and France. Code implementing the described experiments is available at https://github.com/OptimalLockdown.