Information Theoretic Optimal Surveillance for Epidemic Prevalence in Networks

2026-01-07

AI Summary

This work addresses the challenge of resource-constrained and biased networked epidemic surveillance by introducing the TESTPREV problem: selecting a subset of nodes that maximizes mutual information with the true epidemic prevalence to accurately estimate the distribution of outbreak sizes. Departing from conventional approaches that solely optimize detection probability, this study pioneers mutual information maximization as the surveillance objective, revealing the suboptimality of existing strategies under this criterion. Building upon the independent cascade (IC) model, the authors propose GREEDYMI, a general-purpose algorithm that integrates mutual information estimation with greedy optimization, and further enhance its computational efficiency for specific network structures. Experimental results demonstrate that GREEDYMI substantially outperforms baseline methods under the IC model, achieving higher mutual information and significantly reducing the expected variance in epidemic size estimation.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or nodes) to minimize objectives such as probability of outbreak detection. Such methods do not give insights into the outbreak size distribution which, despite being complex and multi-modal, is very useful in public health planning. We introduce TESTPREV, a problem of choosing a subset of nodes which maximizes the mutual information with disease prevalence, which directly provides information about the outbreak size distribution. We show that, under the independent cascade (IC) model, solutions computed by all prior disease surveillance approaches are highly sub-optimal for TESTPREV in general. We also show that TESTPREV is hard to even approximate. While this mutual information objective is computationally challenging for general networks, we show that it can be computed efficiently for various network classes. We present a greedy strategy, called GREEDYMI, that uses estimates of mutual information from cascade simulations and thus can be applied on any network and disease model. We find that GREEDYMI does better than natural baselines in terms of maximizing the mutual information as well as reducing the expected variance in outbreak size, under the IC model.

Problem

• epidemic prevalence
• network surveillance
• mutual information
• outbreak size distribution
• information theoretic

Innovation

• mutual information
• epidemic surveillance
• network epidemiology
• TESTPREV
• GREEDYMI