Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Cui, Jiaming, Adhikari, Bijaya, Haddadan, Arash, Haque, A S M Ahsan-Ul, Vreeken, Jilles, Vullikanti, Anil, Prakash, B. Aditya
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Research Institute, Georgia Institute of Technology, National Institutes of Health, Centers for Disease Control and Prevention, Global Infectious Diseases Institute, University of Virginia, National Institute of General Medical Sciences, Oak Ridge National Laboratory
Awards: CONTACT NETWORK TRANSMISSION MODELING OF HEALTHCARE ASSOCIATED INFECTIONS, III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections, RAPID: Collaborative Research: Using Phylodynamics and Line Lists for Adaptive COVID-19 Monitoring, III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections, III: Medium: Towards Data-Efficient Decision-Focused Learning: A Modularized Pre-Training and Fine-Tuning Approach, G5260964573, BIGDATA: Collaborative Research: F: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics, CAREER: Bridging the Data-Model Gap -- Leveraging Surveillance for Propagation Mining over Networks, G5897046763, III: Small: Advances in Federated Graph Machine Learning: A Data Perspective, RAPID: Collaborative Research: Using Phylodynamics and Line Lists for Adaptive COVID-19 Monitoring, III: Medium: Collaborative Research: Principled Uncertainty Quantification in Deep Learning Models for Time Series Analysis, G6671297155, Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology, Detection and characterization of critical under-immunized hotspots, IN SILICO RANDOMIZED CONTROL TRIAL FRAMEWORK FOR ASSESSING INFECTION CONTROL AND PREVENTION INTERVENTIONS IN THE HOSPITAL, Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology
OpenAlex ID: W4407122971