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A Bayesian Ensemble Approach for Epidemiological Projections
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering. Colorado State University, CO 80523 USA; US National Institute Heatlh, MD USA; University of Exeter, England.ORCID iD: 0000-0001-7856-2925
US National Institute Heatlh, MD USA; University of Nottingham, England.
Colorado State University, CO 80523 USA; US National Institute Heatlh, MD USA.
2015 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 4, e1004187- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Public Library of Science , 2015. Vol. 11, no 4, e1004187- p.
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:liu:diva-119264DOI: 10.1371/journal.pcbi.1004187ISI: 000354517600035PubMedID: 25927892OAI: oai:DiVA.org:liu-119264DiVA: diva2:820642
Note

Funding Agencies|RAPIDD program of the Science and Technology Directorate; Fogarty International Center, National Institutes of Health

Available from: 2015-06-12 Created: 2015-06-12 Last updated: 2017-12-04

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