Investigating heterogeneity in pneumococcal transmission:: A Bayesian-MCMC approach applied to a follow-up of schools

The analysis of communicable agent transmission from field data is typically hampered by missing data, dependence between individual trajectories, and sometimes by heterogeneity among competing pathogens. Methods based on data augmentation and Markov chain Monte Carlo sampling have been used to analyze such data in small communities (typically households), with little diversity in pathogens. In this article the approach is extended to, analyze the transmission of 15 Streptococcus pneumoniae serotypes in schoolchildren, where hundreds of individual trajectories interact and a substantial portion of trajectories are unobserved. For each child, the data were augmented to describe the detailed time course of S. pneumoniae carriage. The Bayesian hierarchical model ensured consistency between observed and augmented data; described the latent dynamics of S. pneumoniae acquisition and clearance; and specified priors. To investigate heterogeneity among serotypes, a clustering step was introduced to select a parsimonious description of transmission characteristics. The joint posterior distribution of parameters, augmented data, and clusters of serotypes was explored by reversible-jump MCMC sampling. The approach made it possible to make inferences simultaneously on the number of clusters of serotypes and on the transmission characteristics of each cluster.