Pizzuti Clara, Socievole Annalisa, Prasse Bastian, Van Mieghem Piet
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via P. Bucci, 8-9C, 87036 Rende, Italy.
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands.
Appl Netw Sci. 2020;5(1):91. doi: 10.1007/s41109-020-00333-8. Epub 2020 Nov 17.
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.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒最初在中国城市武汉出现，随后几乎传播到全球，引发了一场大流行。在中国的情况下，该病毒在中国的接触网络上相当符合易感-感染-康复（SIR）疫情模型。在本文中，我们也研究了SIR模型在意大利网络上的预测准确性。具体而言，意大利各地区是由网络节点代表的一个复合种群，网络链接是这些地区之间的相互作用。然后，我们修改基于网络的SIR模型，以考虑意大利政府在COVID-19传播的各个阶段采取的不同封锁措施。我们的结果表明，当将随时间变化的封锁协议纳入经典SIR模型时，基于网络的模型能更好地预测每日累计感染个体。
