Information Theoretic Optimal Surveillance for Epidemic Prevalence in Networks

TL;DR

This paper introduces TESTPREV, an information-theoretic approach to epidemic surveillance in networks, which optimizes the selection of nodes to better understand outbreak size distribution, surpassing prior methods in effectiveness.

Contribution

The paper proposes TESTPREV, a novel mutual information-based framework for epidemic surveillance, and presents GREEDYMI, an efficient greedy algorithm for its implementation.

Findings

GREEDYMI outperforms baseline methods in maximizing mutual information.

TESTPREV provides better insights into outbreak size distribution.

Mutual information computation is efficient for certain network classes.

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.