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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.
Highlights
The COVID-19 pandemic has put quantitative decision-making methods in the spotlight
In the first half of the section, as a proof of concept, we show the importance of the various terms included in the cost functional and their influence on the results, and this is done for the England dataset with parameter values calibrated with data between the 1st of March and the 23rd of May, with lockdown strategies applied for 90 or 120 days starting on the 24th of May
We apply our methodology to two populations: England and France, with the models calibrated up to the 31st of August and lockdown strategies applied from the 1st of September
Summary
The COVID-19 pandemic has put quantitative decision-making methods (and the lack thereof) in the spotlight. A vast amount of research efforts has been dedicated to model the COVID-19 pandemic focusing on the various aspects of the system dynamics, such as estimating the value of the basic reproductive number [1, 2], evaluating the effect of containment measures and travel restrictions [2,3,4,5,6], assessing the effect of age on the transmission and severity of the disease [1, 7] and estimating the impact on Health Services [8] It is remarkably hard, if not impossible, to capture every aspect of this complex phenomenon in an integrated and computationally tractable mathematical model. We would like to stress here that, in order to define the model dynamics, we have considered some assumptions (on the contact matrices, the connection between mobility data and contacts, the initialisation of the compartments and the definition of the cost functional) which may not be tested in real a life scenario
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