The impact of network clustering and assortativity on epidemic behaviour
Jennifer Badham and
Rob Stocker
Theoretical Population Biology, 2010, vol. 77, issue 1, 71-75
Abstract:
Epidemic models have successfully included many aspects of the complex contact structure apparent in real-world populations. However, it is difficult to accommodate variations in the number of contacts, clustering coefficient and assortativity. Investigations of the relationship between these properties and epidemic behaviour have led to inconsistent conclusions and have not accounted for their interrelationship. In this study, simulation is used to estimate the impact of social network structure on the probability of an SIR (susceptible-infective-removed) epidemic occurring and, if it does, the final size. Increases in assortativity and clustering coefficient are associated with smaller epidemics and the impact is cumulative. Derived values of the basic reproduction ratio (R0) over networks with the highest property values are more than 20% lower than those derived from simulations with zero values of these network properties.
Keywords: Disease spread; Transmission networks; Clustering coefficient; Assortativity; Social networks (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040580909001270
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:thpobi:v:77:y:2010:i:1:p:71-75
DOI: 10.1016/j.tpb.2009.11.003
Access Statistics for this article
Theoretical Population Biology is currently edited by Jeremy Van Cleve
More articles in Theoretical Population Biology from Elsevier
Bibliographic data for series maintained by Catherine Liu ().