Backhausz Ágnes, Kiss István Z, Simon Péter L
Institute of Mathematics, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/c, Budapest, 1117 Hungary.
Alfréd Rényi Institute of Matematics, Reáltanoda utca 13-15, Budapest, 1053 Hungary.
Period Math Hung. 2022;85(2):343-363. doi: 10.1007/s10998-021-00440-8. Epub 2022 Jan 6.
A key factor in the transmission of infectious diseases is the structure of disease transmitting contacts. In the context of the current COVID-19 pandemic and with some data based on the Hungarian population we develop a theoretical epidemic model (susceptible-infected-removed, SIR) on a multilayer network. The layers include the Hungarian household structure, with population divided into children, adults and elderly, as well as schools and workplaces, some spatial embedding and community transmission due to sharing communal spaces, service and public spaces. We investigate the sensitivity of the model (via the time evolution and final size of the epidemic) to the different contact layers and we map out the relation between peak prevalence and final epidemic size. When compared to the classic compartmental model and for the same final epidemic size, we find that epidemics on multilayer network lead to higher peak prevalence meaning that the risk of overwhelming the health care system is higher. Based on our model we found that keeping cliques/bubbles in school as isolated as possible has a major effect while closing workplaces had a mild effect as long as workplaces are of relatively small size.
传染病传播的一个关键因素是疾病传播接触的结构。在当前新冠疫情的背景下，并基于匈牙利人口的一些数据，我们在一个多层网络上开发了一个理论流行病模型（易感-感染-移除，SIR）。这些层包括匈牙利的家庭结构，人口分为儿童、成年人和老年人，以及学校和工作场所，还有由于共享公共空间、服务和公共场所而产生的一些空间嵌入和社区传播。我们研究了模型（通过疫情的时间演变和最终规模）对不同接触层的敏感性，并绘制出峰值患病率与最终疫情规模之间的关系。与经典的 compartments 模型相比，在最终疫情规模相同的情况下，我们发现多层网络上的疫情会导致更高的峰值患病率，这意味着医疗系统不堪重负的风险更高。基于我们的模型，我们发现尽可能使学校中的小群体/小圈子保持隔离有重大影响，而关闭工作场所只要工作场所规模相对较小则影响较小。
