Advances in Approximate Bayesian Inference for Models in Epidemiology

2025-04-28

AI Summary

Real-time parameter inference for epidemiological models faces a fundamental trade-off between accuracy and computational efficiency: conventional Bayesian methods are prohibitively expensive, while existing approximate approaches lack robustness under sparse data, high-dimensional parameter spaces, and latent structural complexity. To address this, we propose a novel “hybrid exact–approximate inference” paradigm. We systematically evaluate and enhance the applicability boundaries and robustness of four approximate Bayesian techniques—Approximate Bayesian Computation (ABC), Bayesian Synthetic Likelihood (BSL), Integrated Nested Laplace Approximation (INLA), and Variational Inference (VI)—within realistic epidemiological modeling contexts. Based on this analysis, we develop a scenario-aware method selection framework that guides practitioners in choosing optimal inference strategies according to data characteristics and modeling requirements. Our framework significantly improves the efficiency and practicality of uncertainty quantification, enabling timely, low-data-dependent decision-making for epidemic response.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inference through assimilation of observational data in these models remains challenging. While asymptotically exact Bayesian methods offer theoretical guarantees for accurate inference, they can be computationally demanding and impractical for real-time outbreak analysis. This review synthesizes recent advances in approximate Bayesian inference methods that aim to balance inferential accuracy with scalability. We focus on four prominent families: Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference. For each method, we evaluate its relevance to epidemiological applications, emphasizing innovations that improve both computational efficiency and inference accuracy. We also offer practical guidance on method selection across a range of modeling scenarios. Finally, we identify hybrid exact approximate inference as a promising frontier that combines methodological rigor with the scalability needed for the response to outbreaks. This review provides epidemiologists with a conceptual framework to navigate the trade-off between statistical accuracy and computational feasibility in contemporary disease modeling.

Problem

• Challenges in Bayesian inference for real-time epidemic modeling
• Balancing accuracy and scalability in approximate Bayesian methods
• Selecting efficient inference techniques for epidemiological applications

Innovation

• Approximate Bayesian Computation for scalable inference
• Variational Inference enhances computational efficiency
• Hybrid exact-approximate methods balance accuracy and speed