Statistical inference for discrete time epidemic models

Project description

Infectious diseases spread in continuous time through interaction between individuals, and many epidemic models are defined in continuous time. However, data are often collected in discrete time (e.g. daily case numbers), and it can be hard to accurately model real-time interactions (e.g. contacting different people at work/home/school at different times of the day). Discrete time epidemic models offer a way to model transmission on a natural time-scale and also have potential advantages for statistical inference. This project will explore the use of discrete time models for epidemics and develop efficient methods for fitting them to data.