Simulation of the impact of people mobility, vaccination rate, and virus variants on the evolution of Covid-19 outbreak

TL;DR

This paper extends a compartmental model to analyze how people mobility, vaccination, and virus variants influence Covid-19 outbreak dynamics, providing insights into epidemic evolution and control strategies.

Contribution

The model now includes vaccination effects, virus variants, and a self-consistent reproduction index, improving the understanding of Covid-19 spread in Italy.

Findings

Model accurately fits epidemic data

Reproduction index correlates with mobility and vaccination

Virus variants significantly impact outbreak evolution

Abstract

We have further extended our compartmental model describing the spread of the infection in Italy. The model is based on the assumption that the time evolution of all of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depend on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides precious information on the tight relationship between the variation of the reported infection cases and a well defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. The extension of the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits very well the epidemic data, and allows us to strictly relate the time evolution of the number of hospitalized case and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.