A Bayesian ensemble approach for epidemiological projections.
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.
Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden; Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America; US National Institute of Health, Bethesda, Maryland, United States of America; University of Exeter, Exeter, United Kingdom.
Tom
T
Lindström
US National Institute of Health, Bethesda, Maryland, United States of America; School of Veterinary Medicine and Science, University of Nottingham, Leicestershire, United Kingdom.
Michael
M
Tildesley
Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America; US National Institute of Health, Bethesda, Maryland, United States of America.
Colleen
C
Webb
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005536:Foot-and-Mouth Disease / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D006113:United Kingdom / Q000453:epidemiology