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A bayesian ensemble approach for epidemiological projections

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Lindström, Tom, Tildesley, Michael J. and Webb, Colleen T. (2015) A bayesian ensemble approach for epidemiological projections. PLoS Computational Biology, 11 (4). e1004187. doi:10.1371/journal.pcbi.1004187

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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1004187

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Abstract

Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.

Item Type: Journal Article
Divisions: Faculty of Science > Life Sciences (2010- )
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 30 April 2015
Dates:
DateEvent
30 April 2015Published
11 February 2015Accepted
1 October 2014Submitted
Volume: 11
Number: 4
Article Number: e1004187
DOI: 10.1371/journal.pcbi.1004187
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

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