A data-augmentation method for infectious disease incidence data from close contact groups

A broad range of studies of preventive measures in infectious diseases gives rise to incidence data from close contact groups. Parameters of common interest in such studies include transmission probabilities and efficacies of preventive or therapeutic interventions. We estimate these parameters using discrete-time likelihood models. We augment the data with unobserved pairwise transmission outcomes and fit the model using the EM algorithm. A linear model derived from the likelihood based on the augmented data and fitted with the iteratively re-weighted least squares method is also discussed. Using simulations, we demonstrate the comparable accuracy and lower sensitivity to initial estimates of the proposed methods with data augmentation relative to the likelihood model based solely on the observed data. Two randomized household-based trials of zanamivir, an influenza antiviral agent, are analyzed using the proposed methods.