Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering.
Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering.
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DOI：
10.1073/pnas.1910181117
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发表时间：
2020-09-22
影响因子：
11.1
通讯作者：
Riley S
中科院分区：
文献类型：
--
作者：
Haw DJ;Pung R;Read JM;Riley S
流行病通常是用一套标准的数学模型来描述的，这些模型并没有捕捉到社会互动，也没有捕捉到地理因素决定这些互动的方式。在这里，我们提出了一个模型，可以反映社交网络的强烈影响，人们的旅行方式，我们表明，他们导致非常不同的流行概况。这种类型的模型可能对预测有用。一些直接传播人类的病原体，如流感和麻疹，使发病率持续呈指数增长，并具有与易感个体迅速减少相一致的高峰发病率。许多人不这样做。虽然在传统的疾病动力学模型中通常会出现延长的指数阶段，但目前对非标准流行病特征的定量描述要么是抽象的，现象学的，要么依赖于网络模型中高度偏斜的后代分布。在这里，我们创建了大型的社会空间网络来表示接触行为，使用人类人口密度数据，以前开发的拟合算法和重力般的移动内核。我们定义了一个基本的再生数为这个系统，类似于用于房室模型。控制，然后，我们探索网络的家庭工作场所的结构，家庭之间的联系可以形成不同程度的空间相关性，由一个单一的参数，从引力核。通过改变这个单一参数和模拟流行病传播，我们能够确定更频繁的局部运动如何导致强空间相关性，从而诱导具有较低、较晚流行病峰值的次指数爆发动态。此外，当运动高度空间相关时，峰高与最终尺寸的比率要小得多。我们调查我们的网络的拓扑性质，通过一个广义的聚类系数，扩展到直接的邻居，确定四阶聚类和非标准的流行病动力学之间非常强的相关性。我们的研究结果激励观察发病率和社会空间的人类行为在流行病表现出非标准的发病模式。
Epidemics are typically described using a standard set of mathematical models that do not capture social interactions or the way those interactions are determined by geography. Here, we propose a model that can reflect social networks influenced strongly by the way people travel, and we show that they lead to very different epidemic profiles. This type of model will likely be useful for forecasting. 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 nonstandard 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 behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number for this system, analogous to that used for compartmental models. Controlling for , 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 subexponential 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 neighborhoods, identifying very strong correlations between fourth-order clustering and nonstandard epidemic dynamics. Our results motivate the observation of both incidence and socio-spatial human behavior during epidemics that exhibit nonstandard incidence patterns.
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影响因子：
4.3
作者：
Farrow DC;Brooks LC;Hyun S;Tibshirani RJ;Burke DS;Rosenfeld R
通讯作者：
Rosenfeld R
影响因子：
11.7
作者：
Chowell G;Sattenspiel L;Bansal S;Viboud C
通讯作者：
Viboud C
影响因子：
6.4
作者：
Griffiths, J;Lowrie, D;Williams, J
通讯作者：
Williams, J
影响因子：
3.8
作者：
Biggerstaff M;Johansson M;Alper D;Brooks LC;Chakraborty P;Farrow DC;Hyun S;Kandula S;McGowan C;Ramakrishnan N;Rosenfeld R;Shaman J;Tibshirani R;Tibshirani RJ;Vespignani A;Yang W;Zhang Q;Reed C
通讯作者：
Reed C
影响因子：
168.9
作者：
Ahuka-Mundeke, Steve;Ahmed, Yahaya Ali;Yuma, Etienne
通讯作者：
Yuma, Etienne