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Epidemics. 2014 Mar;6:37-45. doi: 10.1016/j.epidem.2013.11.002. Epub 2013 Nov 19.

Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy.

Author information

  • 1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
  • 2Section of Integrative Biology, University of Texas at Austin, Austin, TX, USA; Santa Fe Institute, Santa Fe, NM, USA.
  • 3Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
  • 4Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Biostatistics, Yale University, New Haven, CT, USA. Electronic address: jeffrey.townsend@yale.edu.

Abstract

Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions. However, typical sensitivity analyses of deterministic dynamic infectious disease models focus on model architecture and the relative importance of parameters but neglect parameter uncertainty when reporting model predictions. Consequently, model results that identify point estimates of intervention levels necessary to terminate transmission yield limited insight into the probability of success. We apply probabilistic uncertainty analysis to a dynamic model of influenza transmission and assess global uncertainty in outcome. We illustrate that when parameter uncertainty is not incorporated into outcome estimates, levels of vaccination and treatment predicted to prevent an influenza epidemic will only have an approximately 50% chance of terminating transmission and that sensitivity analysis alone is not sufficient to obtain this information. We demonstrate that accounting for parameter uncertainty yields probabilities of epidemiological outcomes based on the degree to which data support the range of model predictions. Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health.

Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

KEYWORDS:

Epidemiology; Health policy; Infectious disease; Mathematical modeling; Uncertainty

PMID:
24593920
[PubMed - indexed for MEDLINE]
PMCID:
PMC4316830
Free PMC Article
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