Complex dynamics of synergistic coinfections on realistically clustered networks
Complex dynamics of synergistic coinfections on realistically clustered networks
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DOI：
10.1073/pnas.1507820112
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发表时间：
2015-08-18
影响因子：
11.1
通讯作者：
Althouse, Benjamin M.
中科院分区：
文献类型：
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作者：
Hebert-Dufresne, Laurent;Althouse, Benjamin M.
我们研究了接触结构聚类对通过单个个体共同感染相互作用的多种疾病动态的影响，这两个问题通常是独立研究的。我们强调，众所周知，聚类会阻碍疾病的传播，但如果耦合的强度与聚类的强度相匹配，那么聚类实际上可以在协同共感染的情况下加速流行病的传播。我们还表明，这种动态会导致流行状态下的一级转变，其中疾病传播性的微小变化可能导致爆炸性爆发，而这些爆炸性爆发只能发生在集群网络上的区域。我们开发了一种遵循易感性-传染性-易感性动态的两种疾病共感染的平均场模型，该模型允许在一般类别的模块化网络上相互作用。我们还引入了基于三级感染的标准，该标准可以对集群何时比非集群网络导致更快的传播产生精确的分析估计。我们的结果对于流行病学、数学建模和相互作用现象的传播具有重要意义。我们呼吁提供有关相互作用的合并感染的更详细的流行病学数据。
We investigate the impact of contact structure clustering on the dynamics of multiple diseases interacting through coinfection of a single individual, two problems typically studied independently. We highlight how clustering, which is well known to hinder propagation of diseases, can actually speed up epidemic propagation in the context of synergistic coinfections if the strength of the coupling matches that of the clustering. We also show that such dynamics lead to a first-order transition in endemic states, where small changes in transmissibility of the diseases can lead to explosive outbreaks and regions where these explosive outbreaks can only happen on clustered networks. We develop a mean-field model of coinfection of two diseases following susceptible-infectious-susceptible dynamics, which is allowed to interact on a general class of modular networks. We also introduce a criterion based on tertiary infections that yields precise analytical estimates of when clustering will lead to faster propagation than nonclustered networks. Our results carry importance for epidemiology, mathematical modeling, and the propagation of interacting phenomena in general. We make a call for more detailed epidemiological data of interacting coinfections.