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

Highlights • Dynamic disease modeling of public health interventions rarely accounts for known uncertainties probabilistically. • Uncertainty distributions for model parameters can be derived by analysis of data. • Probabilistic parameterization of analytical solutions yields outcome uncertainty. • Best point estimate predictions would achieve disease mitigation ∼50% of the time. • Our uncertainty analysis of influenza conveys outcome risk for antiviral and vaccination policy.