Thu, Aug 18, 2022: 8:45 AM-9:00 AM
520E
Background/Question/MethodsEstimates of disease burden are important for setting public health priorities. These estimates involve numerous modeling assumptions, whose uncertainties are not always well described. We developed a framework for estimating the burden of yellow fever in Africa and evaluated its sensitivity to modeling assumptions that are often overlooked.
Results/ConclusionsWe found that alternative interpretations of serological data resulted in a nearly 20-fold difference in burden estimates (range of central estimates, 8.4 × 104 to 1.5 × 106 deaths in 2021–2030). Uncertainty about the vaccination status of serological study participants was the primary driver of this uncertainty. Even so, statistical uncertainty was even greater than uncertainty due to modeling assumptions, accounting for a total of 87% of variance in burden estimates. Combined with estimates that most infections go unreported (range of 95% credible intervals, 99.65 to 99.99%), our results suggest that yellow fever’s burden will remain highly uncertain without major improvements in surveillance.
Results/ConclusionsWe found that alternative interpretations of serological data resulted in a nearly 20-fold difference in burden estimates (range of central estimates, 8.4 × 104 to 1.5 × 106 deaths in 2021–2030). Uncertainty about the vaccination status of serological study participants was the primary driver of this uncertainty. Even so, statistical uncertainty was even greater than uncertainty due to modeling assumptions, accounting for a total of 87% of variance in burden estimates. Combined with estimates that most infections go unreported (range of 95% credible intervals, 99.65 to 99.99%), our results suggest that yellow fever’s burden will remain highly uncertain without major improvements in surveillance.