# Inference in Network-Based Epidemiological Simulations with Probabilistic Programming

Published in AI for Public Health Workshop, ICLR, 2021

Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. This work applies probabilistic programming to infer parameters in agent-based models. We represent mobility networks as degree-corrected stochastic block models and estimate their parameters from cell-phone co-location data. We use these networks in probabilistic programs to simulate the evolution of an epidemic, and condition on reported cases to infer disease transmission parameters. Our experiments demonstrate that the resulting models improve the accuracy-of-fit in multiple geographies relative to baselines that do not model network topology.

https://aiforpublichealth.github.io/papers/ICLR-AI4PH_paper_36.pdf

## Bibtex

@inproceedings{covid-param-inference,
title={Inference in Network-based Epidemiological Simulations with Probabilistic Programming},
author={
Smedemark-Margulies, Niklas and
Walters, Robin and
Zimmermann, Heiko and
Laird, Lucas and
Kaushik, Neela and
Caceres, Rajmonda and
van de Meent, Jan-Willem},
booktitle={AI for Public Health Workshop, ICLR},
year={2021},
url={https://aiforpublichealth.github.io/papers/ICLR-AI4PH_paper_36.pdf}
}