Many important questions in infectious disease epidemiology involve the effects of covariates (e.g., age or vaccination status) on infectiousness and susceptibility, which can be measured in studies of transmission in households or other close-contact groups. Because the transmission of disease produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair ij is the time from the onset of infectiousness in i to infectious contact from i to j, where an infectious contact is sufficient to infect j if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on infectiousness covariates for i, susceptibility covariates for j, and pairwise covariates. This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). In a simulation study, we show that these models produce valid point and interval estimates of parameters governing the contact interval distributions. We also explore the role of epidemiologic study design and the consequences of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection is critical to estimating the effect of antiviral prophylaxis.

翻译：传染病流行病学中的许多重要问题涉及协变量（如年龄或疫苗接种状态）对传染性和易感性的影响，这些可通过家庭或其他密切接触群体中的传播研究进行测量。由于疾病传播会产生相依结局，使用生物统计学中的标准回归模型难以甚至无法解决这些问题。成对生存分析通过计算有序个体对中接触间隔分布的似然来处理相依结局。有序对ij中的接触间隔定义为从i开始具有传染性到i对j产生传染性接触的时间，其中若j易感，则一次传染性接触足以感染j。本文提出一种用于传染病传播的成对加速失效时间回归模型，该模型允许接触间隔分布的速率参数依赖于i的传染性协变量、j的易感性协变量及成对协变量。该模型可同时处理内部感染（由观察对象之间的传播引起）和外部感染（由环境或社区感染源引起）。通过模拟研究，我们证明该模型能生成控制接触间隔分布参数的有效点估计和区间估计。我们还探讨了流行病学研究设计的作用及模型误设的后果。利用该回归模型分析2009年甲型H1N1流感大流行期间洛杉矶县的住户数据，发现考虑外部感染源的能力对于估计抗病毒预防效果至关重要。