A decision-theoretic framework for uncertainty quantification in epidemiological modelling

2025-09-24

AI Summary

Epidemiological modeling lacks a unified framework for uncertainty quantification, leading to inconsistent formalization and classification of uncertainty sources, which hinders model comparison, policy interpretation, and targeted data collection. Method: We propose a decision-theoretic framework that explicitly defines uncertainty as expected loss arising from information deficiency, distinguishes reducible from irreducible uncertainty, and unifies related concepts—from machine learning and experimental design to health economics—via the metric “expected reduction in uncertainty.” Our approach integrates information theory, active learning, and health economics, and is empirically validated using nationwide SARS-CoV-2 wastewater surveillance data from New Zealand. Contribution/Results: The framework enhances interpretability and policy relevance of uncertainty quantification, accurately measures data value in realistic scalability scenarios, and demonstrates practical utility and cross-context generalizability in infectious disease modeling.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Decision/Utility Theory
• Game Theory and Economic Paradigms: Imperfect Information

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sources of uncertainty are rarely formalised, and existing classifications are often defined inconsistently. This lack of structure hampers interpretation, model comparison, and targeted data collection. Connecting ideas from machine learning, information theory, experimental design, and health economics, we present a first-principles decision-theoretic framework that defines uncertainty as the expected loss incurred by making an estimate based on incomplete information, arguing that this is a highly useful and practically relevant definition for epidemiology. We show how reasoning about future data leads to a notion of expected uncertainty reduction, which induces formal definitions of reducible and irreducible uncertainty. We demonstrate our approach using a case study of SARS-CoV-2 wastewater surveillance in Aotearoa New Zealand, estimating the uncertainty reduction if wastewater surveillance were expanded to the full population. We then connect our framework to relevant literature from adjacent fields, showing how it unifies and extends many of these ideas and how it allows these ideas to be applied to a wider range of models. Altogether, our framework provides a foundation for more reliable, consistent, and policy-relevant uncertainty quantification in infectious disease epidemiology.

Problem

• Formalizing inconsistent uncertainty classifications in epidemiological modeling
• Defining uncertainty as expected loss from incomplete information
• Quantifying reducible and irreducible uncertainty for policy decisions

Innovation

• Decision-theoretic framework defining uncertainty as expected loss
• Formal definitions of reducible and irreducible uncertainty
• Case study application to SARS-CoV-2 wastewater surveillance