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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9986

Title: Estimating infectious disease parameters from data on social contacts and serological status
Authors: GOEYVAERTS, Nele
HENS, Niel
Ogunjimi, Benson
AERTS, Marc
SHKEDY, Ziv
Van Damme, Pierre
Beutels, Philippe
Issue Date: 2010
Publisher: Wiley
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 59. p. 255-277
Abstract: In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called 'who acquires infection from whom' matrix. These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Alternatively, we can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may be age-specific characteristics that are related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, we model transmission as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella zoster virus in Belgium. Furthermore, we address the effect on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multimodel inference to deal with model selection uncertainty. The method proposed makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches that have been traditionally applied hitherto.
URI: http://hdl.handle.net/1942/9986
Link to publication: http://arxiv.org/PS_cache/arxiv/pdf/0907/0907.4000v1.pdf
DOI: 10.1111/j.1467-9876.2009.00693.x
ISI #: 000274413000003
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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