Bayesian nonparametric methods for individual-level stochastic epidemic models - Seymour, Rowland G. (2020)
Simulating from and making inference for stochastic epidemic models are key strategies for understanding and controlling the spread of infectious diseases. Current methods for modelling infection rate functions are exclusively parametric. This often involves making strict assumptions about the way the disease spreads and choices which may lack any biological or epidemiological justification. To remove the need for making such assumptions, we develop a Bayesian nonparametric framework which allows us to learn how the disease spreads directly from the data.