Department Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007, Tarragona, Spain.
Department Enginyeria Química, Universitat Rovira i Virgili, 43007, Tarragona, Spain.
Sci Rep. 2019 Feb 19;9(1):2315. doi: 10.1038/s41598-019-38722-4.
The networked structure of contacts shapes the spreading of epidemic processes. Recent advances on network theory have improved our understanding of the epidemic processes at large scale. The relevance of several considerations still needs to be evaluated in the study of epidemic spreading. One of them is that of accounting for the influence of origin and destination patterns in the flow of the carriers of an epidemic. Here we compute origin-destination patterns compatible with empirical data of coarse grained flows in the air transportation network. We study the incidence of epidemic processes in a metapopulation approach considering different alternatives to the flows prior knowledge. The data-driven scenario where the estimation of origin and destination flows is considered turns out to be relevant to assess the impact of the epidemics at a microscopic level (in our scenario, which populations are infected). However, this information is irrelevant to assess its macroscopic incidence (fraction of infected populations). These results are of interest to implement even better computational platforms to forecast epidemic incidence.
接触网络结构塑造了传染病的传播过程。网络理论的最新进展提高了我们对大规模传染病过程的理解。在研究传染病的传播时,还需要评估几个考虑因素的相关性。其中之一是考虑传染病载体流动的起源和目的地模式的影响。在这里,我们计算了与航空运输网络中粗粒度流的经验数据相匹配的起源-目的地模式。我们在考虑不同流动先验知识的复群模型中研究了传染病的发病情况。数据驱动的情景,即考虑起源和目的地流动的估计,对于在微观层面(在我们的情景中,哪些人群被感染)评估传染病的影响是相关的。然而,这一信息对于评估其宏观发病率(感染人群的比例)是无关紧要的。这些结果对于实施甚至更好的计算平台来预测传染病的发病率是有意义的。