Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic |
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| Authors: | Ritabrata Dutta Susana N Gomes Dante Kalise Lorenzo Pacchiardi |
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| Institution: | 1. Department of Statistics, Warwick University, Coventry, United Kingdom ; 2. Department of Mathematics, Warwick University, Coventry, United Kingdom ; 3. School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom ; 4. Department of Statistics, University of Oxford, Oxford, United Kingdom ; The Pennsylvania State University, UNITED STATES |
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| 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. |
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