Abstract
Different types of data can significantly enhance our understanding of the transmission process. For example, studies tracking the spread of an infectious agent within households can help us better understand the determinants of transmission. However, analyzing this data is complicated by the fact that the transmission process is only very imperfectly observed: we know the date when a case’s symptoms began, but we do not know when, where, or by whom that person was infected. In this course, I will describe the inference strategies traditionally employed, which rely in particular on Bayesian data augmentation approaches. I will also show how the analysis of age-structured serological data can be used to reconstruct the history of the circulation of an infectious disease within a population.
