A mathematical model for the COVID-19 pandemic spread in England is presented. The model integrates age-structured Susceptible-Exposed-Infected-Removed dynamics with real mobile phone data accounting for the population mobility  . 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 .
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 URL
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, and we exemplify the applicability of the methodology using datasets from England and France. Code implementing the described experiments is available at URL