Exploiting temporal network structures of human interaction to effectively immunize populations

TL;DR

This paper introduces two vaccination strategies based on recent contact patterns in dynamic social networks, outperforming benchmarks in simulations and real data, to efficiently immunize populations with minimal resource use.

Contribution

The study presents novel temporal contact-based immunization protocols that utilize only local, recent contact information, demonstrating improved efficiency over traditional methods.

Findings

Outperforms benchmark protocols in real data sets

Effective in various epidemic scenarios

Utilizes only past contact information for vaccination decisions

Abstract

If we can lower the number of people needed to vaccinate for a community to be immune against contagious diseases, we can save resources and life. A key to reach such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely to become infected and effective to spread the disease than the average. Fortunately, the very behavior that makes these people important to vaccinate can help us finding them. People you have met recently are more likely to be socially active and thus central in the contact pattern, and important to vaccinate. We propose two immunization schemes exploiting temporal contact patterns. Both of these rely only on obtainable, local information and could implemented in practice. We show that these schemes outperform benchmark protocols in four real data sets under various epidemic scenarios. The data sets are dynamic, which enables us to make more realistic evaluations than other studies - we use information only about the past to perform the vaccination and the future to simulate disease outbreaks. We also use models to elucidate the mechanisms behind how the temporal structures make our immunization protocols efficient.