Complex dynamics of synergistic coinfections on realistically clustered networks

We investigate the impact of contact structure clustering on the dynamics of multiple diseases interacting through coinfection of a single individual, two problems typically studied independently. We highlight how clustering, which is well known to hinder propagation of diseases, can actually speed...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the National Academy of Sciences - PNAS Vol. 112; no. 33; pp. 10551 - 10556
Main Authors Hébert-Dufresne, Laurent, Althouse, Benjamin M.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 18.08.2015
National Acad Sciences
Subjects
Algorithms
Biological Sciences
Cluster Analysis
Coinfection - epidemiology
Communicable Diseases - epidemiology
Computer Simulation
Epidemics
Epidemiology
Humans
Infectious diseases
Mathematical models
Models, Biological
Physical Sciences
influenza
epidemiology
discontinuous transitions
network theory
interacting coinfections
Online AccessGet full text

Cover

More Information
Summary:We investigate the impact of contact structure clustering on the dynamics of multiple diseases interacting through coinfection of a single individual, two problems typically studied independently. We highlight how clustering, which is well known to hinder propagation of diseases, can actually speed up epidemic propagation in the context of synergistic coinfections if the strength of the coupling matches that of the clustering. We also show that such dynamics lead to a first-order transition in endemic states, where small changes in transmissibility of the diseases can lead to explosive outbreaks and regions where these explosive outbreaks can only happen on clustered networks. We develop a mean-field model of coinfection of two diseases following susceptible-infectious-susceptible dynamics, which is allowed to interact on a general class of modular networks. We also introduce a criterion based on tertiary infections that yields precise analytical estimates of when clustering will lead to faster propagation than nonclustered networks. Our results carry importance for epidemiology, mathematical modeling, and the propagation of interacting phenomena in general. We make a call for more detailed epidemiological data of interacting coinfections.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved June 25, 2015 (received for review April 21, 2015)
Author contributions: L.H.-D. and B.M.A. designed research, performed research, analyzed data, and wrote the paper.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1507820112