This repository contains code for the paper:
Anastasia Chatzilena, Edwin van Leeuwen, Oliver Ratmann, Marc Baguelin, Nikolaos Demiris. Contemporary statistical inference for infectious disease models using Stan.
All the examples used in this study can be reproduced by executing the corresponding .Rmd file. The assets directory contains data used in the examples.
Abstract
This paper is concerned with the application of recently developed statistical methods for inference in infectious disease models. We use hierarchical models as well as deterministic and stochastic epidemic processes based upon systems of ordinary differential equations. We illustrate the application of Hamiltonian Monte Carlo and Variational Inference using the freely available software Stan. The methods are applied to real data from outbreak as well as routinely collected observations. The results suggest that both inference methods are feasible in this context and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.