Social Sensors in Epidemiological Networks via Graph Eigenvectors

TL;DR

This paper introduces a spectral graph theory-based method for identifying social sensors in epidemiological networks, enabling early detection of disease spread with fewer monitored nodes, thus improving epidemic management.

Contribution

It proposes a novel, spectral graph theory-driven approach for selecting social sensors, advancing beyond existing methods in epidemiological network analysis.

Findings

Significant improvement over existing methods in synthetic networks

Effective detection of epidemic outbreaks in real-world networks

Method reduces monitoring requirements for early epidemic detection

Abstract

In this paper, we consider epidemiological networks which are used for modeling the transmission of contagious diseases through a population. Specifically, we study the so-called social sensors problem: given an epidemiological network, can we find a small set of nodes such that by monitoring disease transmission on these nodes, we can get ahead of the overall epidemic in the full population? In spite of its societal relevance, there has not been much statistical work on this problem, and we aim to provide an exposition that will hopefully stimulate interest in the research community. Furthermore, by leveraging classical results in spectral graph theory, we propose a novel method for finding social sensors, which achieves substantial improvement over existing methods in both synthetic and real-world epidemiological networks.