Bayesian state-space models for understanding and managing infectious disease challenges

Understanding the ecological and epidemiological processes that govern the transmission of complex multi-host, multi-pathogens systems remains challenging. One of the key reasons is that these are difficult to observe directly, which makes it necessary to rely on less direct, and often ‘weak’, sources of inference. In this talk I will show the power of Bayesian state-space models to overcome some of these difficulties and reveal hidden patterns and relationships from field and experimental data from wildlife and human diseases. Specifically, I will show examples from the Canine Distemper Virus and Canine Parvovirus in lions and dogs in the Serengeti, and mosquito vectors of human Malaria, for which these approaches enabled us to identify the disease dynamics, quantify the impacts of intervention on those dynamics and ultimately identify optimal control strategies for these infectious diseases.