Uploaded December 2020 | Updated September 2026, 2 weeks ago
This IMA event explored the mathematical challenges and opportunities that the COVID-19 epidemic has highlighted, that will go on to be important into the future. Ritabrata Dutta (University of Warwick) was one of the invited speakers at this event and presented a talk on 'Optimal lockdown using Google mobility (joint work with Susana Gomes, Dante Kalise and Lorenzo Pacchiardi).' His full abstract is below:
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.
This IMA event explored the mathematical challenges and opportunities that the COVID-19 epidemic has highlighted, that will go on to be important into the future. Ritabrata Dutta (University of Warwick) was one of the invited speakers at this event and presented a talk on 'Optimal lockdown using Google mobility (joint work with Susana Gomes, Dante Kalise and Lorenzo Pacchiardi).' His full abstract is below:
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.
