# Contemporary statistical inference for infectious disease models using Stan

## TL;DR

This paper explores the use of Hamiltonian Monte Carlo and Variational Inference in Stan for statistical inference of infectious disease models, demonstrating their feasibility and trade-offs in real-world outbreak data analysis.

## Contribution

It introduces the application of advanced Bayesian inference methods to infectious disease modeling using Stan, highlighting their practicality and computational considerations.

## Key findings

- Both methods are computationally feasible for epidemic data.
- There is a trade-off between statistical efficiency and computational speed.
- Real-time outbreak analysis benefits from faster inference 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.