Bayesian Transmission Process Modeling: Epidemics, Invasive Species, and Beyond

Music City Center 

Room: CC-109 

Following the COVID-19 pandemic, there has been an understandable increase in the interest in epidemic and transmission models. However, transmission processes have always been of interest across public health, agriculture and ecology.

Inference for such models is made more complicated by the fact that we often have latent variables (e.g., infection times). Additionally, we often have complex heterogeneities in the population we wish to account for, since, for example, populations do not tend to mix homogeneously. This often leads to a need for spatial and/or network-based models. Typically, inference for such models is done in a Bayesian Markov chain Monte Carlo (MCMC) framework, accounting for latent or uncertain variables such as event times in a data-augmented framework.

In this workshop, we will examine characteristics of, and Bayesian inference for, such transmission models starting with the classic SIR population-level model, and expanding into more complex individual-level models. Topics will be investigated using real epidemic data on Ebola and COVID-19, and via realistic simulation. In addition, we will cover how to fit these models to uncertain data using MCMC. This will be done in R packages, using the packages deSolve, Nimble and Epi-ILM.

Biometrics Section