Contemporary statistical inference for infectious disease models using Stan
Applications
2019-08-09 v3 Quantitative Methods
Abstract
This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and Variational Inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.
Cite
@article{arxiv.1903.00423,
title = {Contemporary statistical inference for infectious disease models using Stan},
author = {Anastasia Chatzilena and Edwin van Leeuwen and Oliver Ratmann and Marc Baguelin and Nikolaos Demiris},
journal= {arXiv preprint arXiv:1903.00423},
year = {2019}
}