Topics in Bayesian inference and model assessment for partially observed stochastic epidemic models - Aristotelous, Georgios (2020)
Stochastic epidemic models can offer a vitally important public health tool for understanding and controlling disease progression. However, these models are of little practical use if they are not supported by data or are not applicable to efficient parameter inference methods. The peculiarities of the epidemic setting, where data are not independent and epidemic processes are rarely fully observed, complicate both model assessment and parameter inference for stochastic epidemic models. Methods for model assessment are not well-established and methods for inference, although more established, still remain inefficient for large-scale outbreaks.