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Many important questions in infectious disease epidemiology involve theeffects of covariates (e.g., age or vaccination status) on infectiousness andsusceptibility, which can be measured in studies of transmission in householdsor other close-contact groups. Because the transmission of disease producesdependent outcomes, these questions are difficult or impossible to addressusing standard regression models from biostatistics. Pairwise survival analysishandles dependent outcomes by calculating likelihoods in terms of contactinterval distributions in ordered pairs of individuals. The contact interval inthe ordered pair ij is the time from the onset of infectiousness in i toinfectious contact from i to j, where an infectious contact is sufficient toinfect j if they are susceptible. Here, we introduce a pairwise acceleratedfailure time regression model for infectious disease transmission that allowsthe rate parameter of the contact interval distribution to depend oninfectiousness covariates for i, susceptibility covariates for j, and pairwisecovariates. This model can simultaneously handle internal infections (caused bytransmission between individuals under observation) and external infections(caused by environmental or community sources of infection). In a simulationstudy, we show that these models produce valid point and interval estimates ofparameters governing the contact interval distributions. We also explore therole of epidemiologic study design and the consequences of modelmisspecification. We use this regression model to analyze household data fromLos Angeles County during the 2009 influenza A (H1N1) pandemic, where we findthat the ability to account for external sources of infection is critical toestimating the effect of antiviral prophylaxis.

Pairwise accelerated failure time regression models for infectious disease transmission in close-contact groups with external sources of infection