Accurately Estimating Unreported Infections using Information Theory

Jiaming Cui; Bijaya Adhikari; Arash Haddadan; A S M Ahsan-Ul Haque; Jilles Vreeken; Anil Vullikanti; B. Aditya Prakash

doi:10.48550/arxiv.2502.00039

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Accurately Estimating Unreported Infections using Information Theory

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Accurately Estimating Unreported Infections using Information Theory

Jiaming Cui, Bijaya Adhikari, Arash Haddadan, A S M Ahsan-Ul Haque, Jilles Vreeken, Anil Vullikanti and B. Aditya Prakash

ArXiV.org

Cornell University

01/26/2025

DOI: 10.48550/arxiv.2502.00039

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Computer Science - Information Theory

Computer Science - Social and Information Networks

Mathematics - Information Theory

Physics - Physics and Society

Details

Title: Subtitle: Accurately Estimating Unreported Infections using Information Theory
Creators: Jiaming Cui
Bijaya Adhikari
Arash Haddadan
A S M Ahsan-Ul Haque
Jilles Vreeken
Anil Vullikanti
B. Aditya Prakash
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2502.00039
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 01/26/2025
Academic Unit: Computer Science
Record Identifier: 9984786447702771

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