title: Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy.
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.
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Gilbert
authors.affiliation: Section of Integrative Biology, University of Texas at Austin, Austin, TX, USA; Santa Fe Institute, Santa Fe, NM, USA.
authors.forename: Lauren Ancel
authors.initials: LA
authors.lastname: Meyers
authors.affiliation: Department 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.
authors.forename: Alison P
authors.initials: AP
authors.lastname: Galvani
authors.affiliation: Program 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.
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Townsend
mesh_terms: D003362:Cost-Benefit Analysis; D006291:Health Policy / Q000191:economics; D006801:Humans; D007252:Influenza Vaccines / Q000008:administration & dosage*; D007251:Influenza, Human / Q000191:economics / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission*; D008432:Mathematical Computing; D008962:Models, Theoretical; D011634:Public Health / Q000191:economics; D013269:Stochastic Processes; D035501:Uncertainty; D014481:United States / Q000453:epidemiology; D014611:Vaccination / Q000191:economics / Q000379:methods
chemical_list: D007252:Influenza Vaccines
keywords: Epidemiology; Health policy; Infectious disease; Mathematical modeling; Uncertainty