A latent variable model for estimating disease transmission rate from data on household outbreaks
A Bayesian latent variable model is proposed for studying household epidemics of infectious diseases in this paper. This model is more general and flexible than the commonly used chain binomial epidemic model. In particular, the model allows for the heterogeneity of the infection transmission rates in related to the sizes and generations of the infectives. Moreover, the model assumes the availability of only the household outbreak sizes, which is more reasonable than assuming the availability of the hardly observed infection chains. The TannerWong's IP algorithm is employed for effective simulations and inferences of this model. Finally, this model was applied to analyzing a real data set on F-4 Asian influenza.
disease transmission rate outbreak size infection chain data augmentation tannerwong algorithm bayesian statistics