10. Joint Compartmental-Survival Bayesian Model and Mediation Analysis with Application to a Canine Cohort Study

Compartmental models can be used to study the epidemiological characteristics of an infectious disease. For example, the susceptible, infectious, susceptible (SIS) model is appropriate for infections that yield no immunity upon recovery. In this study, we expand the SIS framework to account for multiple co-infections among samples from populations at different locations. We propose a fully probabilistic Bayesian compartmental model with random location effects and a survival component to estimate the effects of infection on long-term survival. In particular, a fully Bayesian approach to estimating direct and indirect of effects of treatment on survival. We evaluate these methods through simulation studies and apply them to a longitudinal study in dogs exposed to multiple tick-borne infections and a parasitic infection. We also discuss the benefits of a joint model that incorporates a compartmental model framework to inform survival and compare it to a more traditional survival approach.

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Women in Statistics and Data Science 2024