Accurately Estimating Unreported Infections using Information Theory

2025-01-27

AI Summary

Unreported cases in infectious disease outbreaks lead to inaccurate assessment of transmission dynamics and intervention efficacy. Method: This paper proposes an infection-number inversion method integrating information theory with dynamical modeling. Building upon ordinary differential equation (ODE)-based epidemiological models, it introduces the Minimum Description Length (MDL) principle for parameter calibration—enabling automatic trade-off between model complexity and goodness-of-fit in a data-driven manner—without requiring prior assumptions about case reporting rates. Contribution/Results: Experiments on multi-country COVID-19 time-series data demonstrate that the method reduces average total infection estimation error by 32% compared to conventional calibration approaches. Moreover, it yields more reliable counterfactual simulations of non-pharmaceutical interventions (NPIs), thereby supporting real-time epidemic situational awareness and precision public health response.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly.

Problem

• Unreported Infections
• Epidemic Control
• Disease Severity Assessment

Innovation

• Information Theory
• Ordinary Differential Equations Model
• Epidemic Prediction