Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
arXiv:2606.27286v1 Announce Type: new 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 intensiv
The increasing computational demands of complex epidemiological models, particularly for near-real-time analysis during events like pandemics, necessitate more efficient parameter estimation methods.
Improved simulation-based inference (SBI) can significantly accelerate the speed and accuracy of epidemiological forecasting, leading to more responsive public-health interventions and resource allocation.
The computational bottleneck in Bayesian calibration for certain complex models could be significantly reduced, allowing for quicker iteration and more timely data-driven decisions in public health.
- · Public health organizations
- · Epidemiologists
- · AI/ML researchers in health
- · Government health agencies
- · Traditional MCMC-based modeling approaches
Faster and more accurate modeling of infectious disease spread.
Improved real-time decision-making systems for pandemic response and resource distribution.
Potential for broader application of SBI techniques across other complex scientific and engineering simulations requiring rapid Bayesian parameter estimation.
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Read at arXiv cs.AI