Speaker
Description
Robust parameter inference is central to infectious disease modelling. Traditional Bayesian approaches require an analytical likelihood, which is rarely available for complex models. Classical likelihood-free methods circumvent this limitation through simulation but are inefficient and rely on hand-crafted summary statistics. Neural Posterior Estimation (NPE) is a scalable and flexible alternative that uses deep learning to learn the full posterior distribution \cite{papamakarios_fast_2018}. NPE has shown promise in physics and neuroscience, but is underutilized in infectious disease epidemiology. Here, we evaluate its practical utility for real-world outbreak analysis.
We applied NPE to estimate key epidemiological parameters from two distinct data sources from the 2014 Ebola outbreak in Sierra Leone: (i) time series of reported cases and deaths, and (ii) a phylogeny reconstructed from early viral genome sequences. In both settings, NPE recovered accurate posterior distributions without the need to define any summary statistics.
Our findings demonstrate that NPE is a powerful, general-purpose inference framework for infectious disease modelling. Its flexibility, scalability, and integration within the Bayesian paradigm make it a compelling alternative to traditional inference tools. We advocate for a broader adoption of NPE across epidemiological and phylodynamic applications.
Bibliography
@misc{papamakarios_fast_2018,
title = {Fast $\varepsilon$-free {Inference} of {Simulation} {Models} with {Bayesian} {Conditional} {Density} {Estimation}},
url = {http://arxiv.org/abs/1605.06376},
doi = {10.48550/arXiv.1605.06376},
urldate = {2026-03-09},
publisher = {arXiv},
author = {Papamakarios, George and Murray, Iain},
month = apr,
year = {2018},
note = {arXiv:1605.06376},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Computation},
}
