Network-based Prediction of COVID-19 Epidemic Spreading in Italy

TL;DR

This paper evaluates a network-based SIR model to predict COVID-19 spread in Italy, demonstrating improved accuracy when incorporating time-varying lockdown measures into the model.

Contribution

It introduces a modified network-based SIR model that accounts for different lockdown phases in Italy, enhancing prediction accuracy of epidemic spread.

Findings

Network-based SIR model predicts COVID-19 spread more accurately with lockdown measures.

Incorporating time-varying protocols improves model performance.

Model effectively captures regional interactions and interventions.

Abstract

Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the SIR (Susceptible-Infectious-Recovered) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions. Then, we modify the network-based SIR model in order to take into account the different lockdown measures adopted by the Italian Government in the various phases of the spreading of the COVID-19. Our results indicate that the network-based model better predicts the daily cumulative infected individuals when time-varying lockdown protocols are incorporated in the classical SIR model.