Network-based prediction of COVID-19 epidemic spreading in Italy
- PDF / 2,252,867 Bytes
- 22 Pages / 595.276 x 790.866 pts Page_size
- 20 Downloads / 198 Views
Applied Network Science
Open Access
RESEARCH
Network‑based prediction of COVID‑19 epidemic spreading in Italy Clara Pizzuti1* , Annalisa Socievole1, Bastian Prasse2 and Piet Van Mieghem2 *Correspondence: [email protected] 1 National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via P. Bucci, 8‑9C, 87036 Rende, Italy Full list of author information is available at the end of the article
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 Susceptible–Infectious–Recovered (SIR) 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. Keywords: Network inference, Epidemiology, COVID-19, Coronavirus, SIR model, Transmission modifier
Introduction The outbreak of the greatest epidemic of the twenty first century caused by the SARSCoV-2 virus has stimulated researchers to understand and control the spread of the disease inside a population with the help of mathematical models developed in recent years (Hethcote 2000; Pastor-Satorras et al. 2015). A single outbreak of a disease is typically described by a SIR compartmental model, where each individual at a certain time t can only be in one of the three different disease stages: Susceptible (S), i.e. healthy, but vulnerable for the infection, Infected (I) and Recovered (R), i.e. the individual either recovers from the disease or, unfortunately, dies. A diffusion-like SIR epidemic spread on a contact network models the infection between individuals when they come into contact, close enough in space and long enough in time (Chu et al. 2020). By adopting the SIR model, Prasse et al. (2020) predict the spreading of the COVID-19 epidemic on a contact network consisting of 16 cities in the Chinese province Hubei via their Network Inference-based Prediction Algorithm (NIPA). Since the interactions between cities are unknown, Prasse et al. exploit their network reconstruction approach, described in Prasse and Van Mieghem (2020b), to estimate the contact network from the observations of the viral states.
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and th
Data Loading...
