Is Network Clustering Detectable in Transmission Trees?

David Welch

doi:10.3390/v3060659

Abstract

Networks are often used to model the contact processes that allow pathogens to spread between hosts but it remains unclear which models best describe these networks. One question is whether clustering in networks, roughly defined as the propensity for triangles to form, affects the dynamics of disease spread. We perform a simulation study to see if there is a signal in epidemic transmission trees of clustering. We simulate susceptible-exposed-infectious-removed (SEIR) epidemics (with no re-infection) over networks with fixed degree sequences but different levels of clustering and compare trees from networks with the same degree sequence and different clustering levels. We find that the variation of such trees simulated on networks with different levels of clustering is barely greater than those simulated on networks with the same level of clustering, suggesting that clustering can not be detected in transmission data when re-infection does not occur.

Highlights

To understand the dynamics of infectious diseases it is crucial to understand the structure and interactions within the host population
We report results here for SEIR epidemics run over Bernoulli and power-law networks
The results presented above suggest that the behaviour of an epidemic on a random network with a given degree sequence is relatively unaffected by the level of clustering in the network

Summary

Introduction

To understand the dynamics of infectious diseases it is crucial to understand the structure and interactions within the host population. One promising development in this field is the use of statistical techniques which aim to model a contact network based on data relating to the passage of a pathogen through a population Such data includes infection times [4,5,6] and genetic sequences that are collected from an epidemic present in the population of interest [7,8,9]. These data have previously been shown to be useful for reconstructing transmission histories (the distinction between a contact network and a transmission history is that a contact network includes all edges between hosts across which disease may spread, whereas the transmission history is just the subset of edges across which transmission occurred). Due to the random accumulation of mutations in the sequences, we expect sequences from an infector/infectee pair to be much closer to each other than sequences from a randomly selected pair in the population

Methods

Results

Conclusion