Optimal Disease Outbreak Detection in a Community Using Network Observability

TL;DR

This paper develops a method to select optimal nodes for measurement in a network to maximize disease outbreak observability, using the inverse Gramian determinant, with applications to epidemic detection.

Contribution

It introduces a novel approach to optimize node measurement for disease detection in networks based on observability theory.

Findings

Optimal node selection improves epidemic detection accuracy.

Topology changes affect network observability.

Simulation confirms effectiveness of the proposed method.

Abstract

Given a network, we would like to determine which subset of nodes should be measured by limited sensing facilities to maximize information about the entire network. The optimal choice corresponds to the configuration that returns the highest value of a measure of observability of the system. Here, the determinant of the inverse of the observability Gramian is used to evaluate the degree of observability. Additionally, the effects of changes in the topology of the corresponding graph of a network on the observability of the network are investigated. The theory is illustrated on the problem of detection of an epidemic disease in a community. The purpose here is to find the smallest number of people who must be examined to predict the number of infected people in an arbitrary community. Results are demonstrated in simulation.