Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC

2026-06-25 · 17:03 UTC ·Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker... ·1 min read

Brief

Simulation-based inference (SBI) using neural posterior estimation is evaluated as a fast alternative to MCMC for Bayesian calibration of a mechanistic SECIR model to COVID‑19 ICU occupancy data from Germany (2020). Across 31-day windows SBI recovered MCMC-like posteriors and trajectories; on a challenging 201-day reconstruction SBI preserved key posterior structure while reducing compute from >19,000s to ~157s.

Why it matters

SBI (neural posterior estimation) calibrated a mechanistic SECIR COVID‑19 model on Germany 2020 ICU-occupancy data: for 31-day inference windows SBI matched MCMC posteriors while running ~60–70 seconds on a single GPU versus ~1,000 seconds for MCMC (CPU); for a 201-day reconstruction SBI averaged ~157 seconds vs >19,000 seconds for MCMC.

Key details

Posterior agreement was evaluated with Wasserstein distances, Kullback–Leibler divergences, and posterior predictive checks; SBI accurately reproduced observed ICU trajectories across 31-day windows and preserved the dominant posterior structure in the more uncertain 201-day problem.

SBI leverages neural posterior estimation and combined CPU+GPU resources to provide a rapid, scalable Bayesian calibration alternative to standard MCMC for high-dimensional, nonlinear epidemiological models.

Abstract

Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of such models is commonly performed using Markov chain Monte Carlo (MCMC), which can become computationally expensive for high-dimensional nonlinear systems and repeated near-real-time analyses. Here, we investigate simulation-based inference (SBI) using neural posterior estimation as a scalable alternative for Bayesian calibration of a mechanistic SECIR epidemiological model using COVID-19 intensive care unit (ICU) occupancy data from Germany during 2020. We compared SBI and MCMC across multiple epidemic phases using both 31-day inference windows and a substantially more challenging 201-day reconstruction problem involving multiple transmission change points. Posterior agreement was evaluated quantitatively using Wasserstein distances and Kullback-Leibler divergences together with posterior predictive checks. Across the 31-day windows, SBI recovered posterior distributions in strong agreement with MCMC while accurately reproducing observed ICU trajectories. In the 201-day setting, SBI preserved the dominant posterior structure despite increased uncertainty. SBI, by combining CPU and GPU resources, substantially reduced computational runtime compared with MCMC, which was restricted to running on CPUs. Whereas MCMC required approximately 1000 seconds for the 31-day inference problems, SBI achieved comparable posterior and predictive performance in approximately 60-70 seconds on a single GPU. For the 201-day inference problem, SBI required an average of 157 seconds, while the MCMC runs took over 19,000 seconds. Our results demonstrate that SBI provides a rapid and computationally efficient framework for Bayesian calibration of mechanistic epidemiological models, supporting repeated near-real-time inference and rapid outbreak analysis.