tetano
Editor, Senior Moderator
Appl Netw Sci
. 2020;5(1):91.
doi: 10.1007/s41109-020-00333-8. Epub 2020 Nov 17.
Network-based prediction of COVID-19 epidemic spreading in Italy
Clara Pizzuti[SUP] 1 [/SUP], Annalisa Socievole[SUP] 1 [/SUP], Bastian Prasse[SUP] 2 [/SUP], Piet Van Mieghem[SUP] 2 [/SUP]
Affiliations
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: COVID-19; Coronavirus; Epidemiology; Network inference; SIR model; Transmission modifier.
. 2020;5(1):91.
doi: 10.1007/s41109-020-00333-8. Epub 2020 Nov 17.
Network-based prediction of COVID-19 epidemic spreading in Italy
Clara Pizzuti[SUP] 1 [/SUP], Annalisa Socievole[SUP] 1 [/SUP], Bastian Prasse[SUP] 2 [/SUP], Piet Van Mieghem[SUP] 2 [/SUP]
Affiliations
- PMID: 33225045
- PMCID: PMC7670995
- DOI: 10.1007/s41109-020-00333-8
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: COVID-19; Coronavirus; Epidemiology; Network inference; SIR model; Transmission modifier.