Infection in Social Networks: Using Network Analysis to Identify High-Risk Individuals

TLDR

Centrality describes an individual's position in a population and can be measured by various parameters. The study assessed whether network centrality measures can identify high‑risk individuals. The authors simulated SIR outbreaks on small‑world and random networks and evaluated several centrality metrics—degree, random‑walk betweenness, shortest‑path betweenness, and farness—to estimate infection risk and timing. Simulations showed that small‑world networks spread infection faster yet infected fewer individuals, and all centrality metrics correlated with infection risk and timing, with degree performing as well as more complex measures, suggesting that targeting highly central individuals could improve surveillance and control.

Abstract

Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. "Centrality" describes an individual's position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies.