Burden is in the eye of the beholder: Sensitivity of yellow fever disease burden estimates to modeling assumptions

Estimates 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. We found that alternative interpretations of serological data resulted in a nearly 20-fold difference in burden estimates (range of central estimates, 8.4 x 10(4) to 1.5 x 10(6) 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.

Automated summary

Estimates of disease burden are crucial for public health priorities, but uncertainties are high. When estimating the burden of yellow fever in Africa, alternative interpretations of data and statistical uncertainties play significant roles, highlighting the need for improved surveillance.