This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016–2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

本文介绍了对疾病传播背景下出现的社交网络进行建模和分析的新方法。提出了四种构建信息网络的方法，其中两种使用。静态数据和两个使用时间数据，即在很长一段时间内进行的个人公民流动性观察。我们展示了如何使用基于网络排列的意外分析来分析构建的网络，以及如何解释数值结果。在此过程中，我们解释了意外分析与传统网络假设检验和二次分配过程回归的关系。惊喜分析更全面，可以不受限制地使用任何形式的网络子图进行，包括具有多个节点属性、加权链接和时间特征的那些。为了说明我们在应用中的方法论工作，我们将它们用于解释根据一年多收集的数据构建的网络，该网络是在 2016-2017 年流感季节期间在纽约州布法罗和伊利县进行的一项观察性研究中。即使数据量有限，我们的方法也能够揭示流感传播的全球（城市和季节范围）模式，同时考虑到人口流动性和社会经济因素。