Pairwise accelerated failure time models for infectious disease transmission with external sources of infection
share
Parametric survival analysis can be used to handle dependent happenings in infectious disease transmission data by estimating failure time distributions in ordered pairs of individuals rather than individuals. The failure time 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 he or she is susceptible. This failure time is called the contact interval, and its distribution can be used to calculate transmission probabilities and show how infectiousness changes over time in infected individuals. Many important questions in infectious disease epidemiology involve the effects of covariates (e.g., age or vaccination status) on the risk of transmission. Here, we generalize earlier pairwise methods in two ways: First, we introduce a pairwise accelerated failure time model in which the rate parameter of the contact interval distribution can depend on infectiousness covariates for i, susceptibility covariates for j, or pairwise covariates. Second, we show how internal infections (caused by individuals under observation) and external infections (caused environmental or community sources not under observation) can be handled simultaneously. In a series of simulations, we show that these methods produce valid point and interval estimates of coefficients and that the ability to account for external infections is critical to consistent estimation. Finally, we use these methods to analyze household surveillance data from Los Angeles County during the 2009 influenza A(H1N1) pandemic.
READ FULL TEXT
