A High-Resolution Human Contact Network for Infectious Disease Transmission

TL;DR

This study uses high-resolution wireless sensor data to map a human contact network in a high school, revealing its structure and informing more effective disease immunization strategies.

Contribution

It provides the first detailed high-resolution contact network for infectious disease modeling in a real-world setting, enabling improved intervention strategies.

Findings

High-density contact network with small-world properties

Computer simulations align with real influenza data

Targeted immunization based on contact data outperforms random strategies

Abstract

The most frequent infectious diseases in humans - and those with the highest potential for rapid pandemic spread - are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At a 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 meters among 788 individuals. The data revealed a high density network with typical small world properties and a relatively homogenous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.