Department of Biology, Georgetown University, Washington, DC 20057;
Department of Biology, Georgetown University, Washington, DC 20057.
Proc Natl Acad Sci U S A. 2017 Apr 18;114(16):4165-4170. doi: 10.1073/pnas.1613616114. Epub 2017 Apr 3.
Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.
疾病风险是群体生活的潜在代价。虽然模块化组织被认为可以降低动物社会的这种代价，但支持这一假设的经验证据一直存在冲突。我们分析了来自 43 个动物物种的经验社会网络，以激发我们对动物社会模块化结构的流行病学后果的研究。从这些经验研究中，我们确定了与网络模块化相关的互动模式特征，并开发了一个理论网络模型，以研究社会网络的细分何时以及如何影响疾病动态。与之前的工作相反，我们发现疾病风险在很大程度上不受模块化结构的影响，尽管超过模块化阈值的社会网络的疾病负担较小，疾病持续时间较长。我们的研究结果表明，高度模块化社会网络中疾病负担的降低是由模块化组织的两种机制驱动的：网络碎片化和亚群内聚性。具有内聚亚群的高度碎片化的社会网络能够将感染在少数几个亚群内结构性地困住，并对疾病爆发的传播造成结构性延迟。最后，我们表明，只有在有先验知识表明群体内的相互作用高度细分的情况下，才需要纳入模块化结构的网络模型。否则，当需要有限的数据估计流行病后果时，基于群体规模和局部接触异质性的基本知识的空网络可能就足够了。总的来说，我们的工作并不支持模块化结构普遍减轻群体生活疾病影响的假设。
