CSH Talk by Giulio Colombini

The COVID-19 pandemic has significantly impacted human society at many levels, from public health to economics and transport, highlighting the need for approaches integrating all available information to better understand and model similar phenomena, also to develop early detection and responses.
We show the result of an analysis of the COVID-19 pandemic in the metropolitan area of Bologna, Italy, integrating an epidemiological mathematical model, clinical hospitalization, vaccination campaign, and human mobility data in the period 2020-2022.

We were able to follow the evolution of the epidemic, observing the effect of vaccination and other factors that produced significant changes in hospitalizations. Moreover, by considering a mathematical model of COVID-19 epidemics spread, with parameters selected partly from literature and partly adapted to the local situation on a weekly basis, we identified a strict relation between human mobility at the mesoscopic level and a sociality rate (related to the model reproduction number) revealing the mutual impact of health issues on human activity and vice versa. A semi-analytical sensitivity study was also performed on the model, so to quantify the impact of uncertainties and noise in the estimated parameters at different times during an epidemic wave.