Spread of infectious diseases through clustered populations

TL;DR

This paper develops analytical methods to understand how clustering, heterogeneity in infectiousness and susceptibility, and contact closeness influence the spread of infectious diseases in social networks, providing insights for intervention strategies.

Contribution

It introduces a systematic approach to incorporate clustering effects into infectious disease models, highlighting their impact on epidemic growth, probability, and size.

Findings

Clustering mainly affects epidemic growth rate.

Heterogeneity in infectiousness influences epidemic probability.

Heterogeneity in susceptibility determines epidemic size.

Abstract

Networks of person-person contacts form the substrate along which infectious diseases spread. Most network-based studies of the spread focus on the impact of variations in degree (the number of contacts an individual has). However, other effects such as clustering, variations in infectiousness or susceptibility, or variations in closeness of contacts may play a significant role. We develop analytic techniques to predict how these effects alter the growth rate, probability, and size of epidemics and validate the predictions with a realistic social network. We find that (for given degree distribution and average transmissibility) clustering is the dominant factor controlling the growth rate, heterogeneity in infectiousness is the dominant factor controlling the probability of an epidemic, and heterogeneity in susceptibility is the dominant factor controlling the size of an epidemic. Edge weights (measuring closeness or duration of contacts) have impact only if correlations exist between different edges. Combined, these effects can play a minor role in reinforcing one another, with the impact of clustering largest when the population is maximally heterogeneous or if the closer contacts are also strongly clustered. Our most significant contribution is a systematic way to address clustering in infectious disease models, and our results have a number of implications for the design of interventions.