European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Inferring Mobility Distributions in Heterogeneous Epidemic Models

MS134-04

15 Jul 2026, 12:10

20m

15.03 - HS (University of Graz)

Minisymposium Talk Mathematical Epidemiology Novel Data Streams for Disease Modeling: Challenges and Opportunities

Speaker

Weiqi Chu (University of Massachusetts)

Description

Classical compartmental models often overestimate the size of a pandemic due to the assumption of a homogeneous population. At the early stage of an outbreak, individuals with higher mobility are more likely to become infected, resulting in an inflated estimate of the final pandemic size. In this talk, we introduce a heterogeneous compartmental model in which each individual has different mobility levels. We develop a deep-learning framework to infer the mobility distribution from partial observations of epidemic dynamics. We also present theoretical results showing that these partial observations uniquely determine the mobility distribution, which establishes that the corresponding inverse problem is well-posed.

Bibliography

@article{doi:10.1137/24M1691557,
author = {Jiang, Ning and Chu, Weiqi and Li, Yao},
title = {Modeling, Inference, and Prediction in Mobility-Based Compartmental Models for Epidemiology},
journal = {SIAM Journal on Applied Mathematics},
volume = {85},
number = {5},
pages = {2355-2375},
year = {2025},
doi = {10.1137/24M1691557},
URL = {https://doi.org/10.1137/24M1691557},
eprint = { https://doi.org/10.1137/24M1691557}
}