Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty

TL;DR

This paper develops a multiscale spatial model with uncertainty to simulate COVID-19 spread in Italy, combining kinetic transport and diffusion equations for commuters and non-commuters, calibrated with real data.

Contribution

It introduces a coupled kinetic-diffusion multiscale model with uncertainty quantification for epidemic spread, specifically applied to Italy's COVID-19 outbreak.

Findings

Model accurately describes the spatial expansion of COVID-19 in Italy.

Incorporates uncertainty in initial data and parameters.

Successfully calibrates with real geographical and epidemic data.

Abstract

In this paper we introduce a space-dependent multiscale model to describe the spatial spread of an infectious disease under uncertain data with particular interest in simulating the onset of the COVID-19 epidemic in Italy. While virus transmission is ruled by a SEIAR type compartmental model, within our approach the population is given by a sum of commuters moving on a extra-urban scale and non commuters interacting only on the smaller urban scale. A transport dynamic of the commuter population at large spatial scales, based on kinetic equations, is coupled with a diffusion model for non commuters at the urban scale. Thanks to a suitable scaling limit, the kinetic transport model used to describe the dynamics of commuters, within a given urban area coincides with the diffusion equations that characterize the movement of non-commuting individuals. Because of the high uncertainty in the data reported in the early phase of the epidemic, the presence of random inputs in both the initial data and the epidemic parameters is included in the model. A robust numerical method is designed to deal with the presence of multiple scales and the uncertainty quantification process. In our simulations, we considered a realistic geographical domain, describing the Lombardy region, in which the size of the cities, the number of infected individuals, the average number of daily commuters moving from one city to another, and the epidemic aspects are taken into account through a calibration of the model parameters based on the actual available data. The results show that the model is able to describe correctly the main features of the spatial expansion of the first wave of COVID-19 in northern Italy.