论文标题
在意大利，基于网络的Covid-19流行病的预测
Network-based Prediction of COVID-19 Epidemic Spreading in Italy
论文作者
论文摘要
SARS-COV-2病毒最初出现在中国城市武汉（Wuhan），随后几乎在全球范围内传播，引起了大流行，因此在中国案件中，在接触网络上，SIR（易感性 - 感染性复发）的流行模型相当出色。在本文中，我们研究了SIR模型对意大利网络的预测准确性。具体而言，意大利区域是由网络节点表示的群，网络链接是这些区域之间的交互。然后，我们修改了基于网络的SIR模型，以考虑意大利政府在COVID-19的各个阶段采用的不同锁定措施。我们的结果表明，基于网络的模型可以在经典的SIR模型中纳入时变化的锁定协议时可以更好地预测每日累积感染的个体。
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.
