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Abstract:
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Kenah(2011) showed that parametric survival analysis can be used to handle dependent happenings in infectious disease transmission data by taking ordered pairs of susceptible-infected individuals as the units of analysis. In this approach, the failure time the contact interval, the time from the onset of infectiousness in an individual i to infectious contact from i to individual j, where an infectious contact is sufficient to infect j if he/she is susceptible. These methods assumed the same contact interval distribution in all pairs. We generalize pairwise survival analysis in two ways: First, introduce a pairwise accelerated failure time model in which the rate parameter of the contact interval distribution depends on covariates associated with infectiousness in i and susceptibility in j. Second, we show how internal infections (within a household) and external infections (sourced from outside) can be handled simultaneously. In simulations, we show that these methods produce valid point and interval estimates of transmission probabilities and rate ratios. We use these methods to analyze influenza A(H1N1) surveillance data from Los Angeles County during the 2009 pandemic
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