Contact (or mixing, or more generally connectivity) matrices are a fundamental component of modelling and inference for infectious disease epidemiology. Their structure and parametrisation directly accounts for the frequency of interactions between different subpopulations of individuals, as well as having the potential to encode dynamic heterogeneity in these interactions across demographic axes, space and time. Considerable research has been devoted to the structure and estimation of (components of) these matrices to help inform outbreak control and forecast disease spread. In this paper, we review the existing literature on the data types used to construct contact matrices and the methods for incorporating uncertainties and heterogeneities into them. We also highlight remaining challenges and future directions in the use of these contact matrices for epidemiological research.

翻译：接触（或混合，更广义地称为连接）矩阵是传染病流行病学建模与推断的基础组成部分。其结构与参数化直接反映了不同亚人群之间的交互频率，并具备在人口学维度、空间维度和时间维度上编码这些交互动态异质性的能力。大量研究致力于这些矩阵（的组成部分）的结构与估计，以协助疫情暴发控制与疾病传播预测。本文系统综述了构建接触矩阵所采用的数据类型，以及将不确定性和异质性纳入其中的方法，并指出了当前在流行病学研究中应用这些接触矩阵所面临的挑战与未来方向。