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Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling

机译:俄罗斯城市的联系模式和流感爆发:通过基于代理的建模的概念证明研究

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In this paper we model influenza propagation in the Russian setting using a spatially explicit model and a detailed human agent database as its input. The aim of our research is to assess the applicability of this modeling method using influenza incidence data for the 2010-2011 epidemic outbreak in Saint Petersburg, and to demonstrate the influence of human contact patterns on the simulated influenza dynamics with a real-case scenario. For this purpose, we use several types of synthetic populations which reflect different ways of distributing people among the workplaces and different contact patterns within the households and the workplaces. The agent-based model is implemented to perform the simulations using Python programming language. We show that in the populations where intensive mixing of people occurs, the influenza outbreaks have higher incidence peak and shorter duration, compared to the populations with less variety of contacts. Hence, the features of contact patterns may dramatically alter the course of epidemics in Russian cities and explain the differences between their outbreak dynamics in the situation when infection virulence and the average number of contacts stay the same. (C) 2020 Elsevier B.V. All rights reserved.
机译:在本文中,我们使用空间显式模型和详细的人工代理数据库作为其输入,模拟俄罗斯环境中的流感传播。我们的研究目的是评估使用2010 - 2011年疫情爆发的流感发病率数据的这种建模方法的适用性,并展示了具有实际情况的模拟流感动态对人的接触模式的影响。为此目的,我们使用几种类型的合成群体,反映了在工地和家庭和工作场所内的不同联系模式之间分配人的不同方式。基于代理的模型用于使用Python编程语言执行模拟。我们表明,在人们发生密集混合的情况下,流感爆发具有较高的发病率高,持续时间较短,与具有较少各种接触的人口相比。因此,联系模式的特征可以大大改变俄罗斯城市中的流行病课程,并在感染毒力和平均触点的平均数保持相同时解释其爆发动力学之间的差异。 (c)2020 Elsevier B.v.保留所有权利。

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