Skip to main content

Inferring population-level contact heterogeneity from common epidemic data

Author(s): Stack, J. Conrad; Bansal, Shweta; Kumar, V. S. Anil; Grenfell, Bryan T.

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr12984
Full metadata record
DC FieldValueLanguage
dc.contributor.authorStack, J. Conrad-
dc.contributor.authorBansal, Shweta-
dc.contributor.authorKumar, V. S. Anil-
dc.contributor.authorGrenfell, Bryan T.-
dc.date.accessioned2019-04-19T18:35:43Z-
dc.date.available2019-04-19T18:35:43Z-
dc.date.issued2012-11-08en_US
dc.identifier.citationStack, J. Conrad, Bansal, Shweta, Kumar, V. S. Anil, Grenfell, Bryan T. (2012). Inferring population-level contact heterogeneity from common epidemic data. Journal of The Royal Society Interface, 10 (78), 20120578 - 20120578. doi:10.1098/rsif.2012.0578en_US
dc.identifier.issn1742-5689-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr12984-
dc.description.abstractModels of infectious disease spread that incorporate contact heterogeneity through contact networks are an important tool for epidemiologists studying disease dynamics and assessing intervention strategies. One of the challenges of contact network epidemiology has been the difficulty of collecting individual and population-level data needed to develop an accurate representation of the underlying host population’s contact structure. In this study, we evaluate the utility of common epidemiological measures (R0, epidemic peak size, duration and final size) for inferring the degree of heterogeneity in a population’s unobserved contact structure through a Bayesian approach. We test the method using ground truth data and find that some of these epidemiological metrics are effective at classifying contact heterogeneity. The classification is also consistent across pathogen transmission probabilities, and so can be applied even when this characteristic is unknown. In particular, the reproductive number, R0, turns out to be a poor classifier of the degree heterogeneity, while, unexpectedly, final epidemic size is a powerful predictor of network structure across the range of heterogeneity. We also evaluate our framework on empirical epidemiological data from past and recent outbreaks to demonstrate its application in practice and to gather insights about the relevance of particular contact structures for both specific systems and general classes of infectious disease. We thus introduce a simple approach that can shed light on the unobserved connectivity of a host population given epidemic data. Our study has the potential to inform future data-collection efforts and study design by driving our understanding of germane epidemic measures, and highlights a general inferential approach to learning about host contact structure in contemporary or historic populations of humans and animals.en_US
dc.format.extent20120578 - 20120578en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of The Royal Society Interfaceen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleInferring population-level contact heterogeneity from common epidemic dataen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1098/rsif.2012.0578-
dc.date.eissued2012-10-03en_US
dc.identifier.eissn1742-5662-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

Files in This Item:
File Description SizeFormat 
Inferring_population_level_contact_2012.pdf984.97 kBAdobe PDFView/Download


Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.