Some directly transmitted human pathogens such as influenza and measles generate sustained exponential growth in incidence, and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of non-standard epidemic profiles are either abstract, phenomenological or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behaviour using human population density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number R₀ for this system analogous to that used for compartmental models. Controlling for R₀, we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and thus induce sub-exponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighbourhoods, identifying very strong correlations between 4th order clustering and non-standard epidemic dynamics. Our results motivate the joint observation of incidence and socio-spatial human behaviour during epidemics that exhibit non-standard incidence patterns.

中文翻译：

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具有强大空间嵌入的社交网络会生成由高阶聚类驱动的非标准流行病动态

一些直接传播的人类病原体（例如流感和麻疹）会导致发病率持续指数增长，并且与易感个体的迅速枯竭相一致的高峰发病率很高。许多人没有。虽然延长的指数期通常出现在传统的疾病动力学模型中，但当前对非标准流行病概况的定量描述要么是抽象的，现象学的，要么是依赖于网络模型中高度倾斜的后代分布。在这里，我们使用人口密度数据，先前开发的拟合算法和类似重力的移动性内核来创建大型的社会空间网络来表示接触行为。我们定义基本生殖数R ₀该系统类似于隔间模型。控制R ₀，然后，我们探索一种具有家庭-工作场所结构的网络，其中可以通过不同程度的空间相关性来形成家庭之间的联系，而空间相关性是由类似重力的内核中的单个参数确定的。通过更改此单个参数并模拟流行病传播，我们能够确定更频繁的局部运动如何导致强烈的空间相关性，从而诱发流行病高峰更低，随后更低的亚指数暴发动态。而且，当运动在空间上高度相关时，峰高与最终大小的比值要小得多。我们通过扩展到邻近社区之外的广义聚类系数来研究网络的拓扑特性，从而确定四阶聚类与非标准流行病动力学之间的非常强的相关性。