Paper Detail
Laura M. Guzmán-Rincón, George R. E. Bradley, Joel Kandiah, Kyriakos Flouris, Pietro Liò, Paul J. Birrell, Alexander E. Zarebski, Daniela De Angelis
The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to use in real-time. Amortized simulation-based inference promises to overcome this difficulty by exploiting the power of machine learning (ML) to perform approximate forecasting at near-real-time using arbitrarily complex models of epidemics. In this work we introduce Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens. GENIE is designed to reflect two key characteristics of outbreaks: (i) shared biological mechanisms across locations and (ii) location-specific characteristics affecting transmission dynamics. This results in the model architecture having two modules: (i) a Local Infection Encoder - which learns to represent disease dynamics shared across all locations and (ii) a Local Profile Encoder - which learns location-specific representations. Using simulations from a high-resolution spatio-temporal ABM, GENIE is trained to generate samples from an approximate posterior predictive distribution of future epidemic trajectories. Benchmarked against established statistical and ML models, GENIE demonstrates superior performance across a range of measures including the timing and magnitude of peak hospitalisations.
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@article{guzmnrincn2026genie,
title = {GENIE: Generative Neural Inference for Epidemics},
author = {Laura M. Guzmán-Rincón and George R. E. Bradley and Joel Kandiah and Kyriakos Flouris and Pietro Liò and Paul J. Birrell and Alexander E. Zarebski and Daniela De Angelis},
year = {2026},
abstract = {The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to u},
url = {https://arxiv.org/abs/2608.20253},
keywords = {stat.ME, q-bio.QM},
eprint = {2608.20253},
archiveprefix = {arXiv},
}
{}