In this study, we explored how alternative assumptions about the interpretation of serological data and alternative approaches to regression modeling impact estimates of yellow fever burden in Africa and projections of future vaccination impact. Numerous other forms of model uncertainty remain unexplored. have begun to address model uncertainty by taking weighted averages of models that represent alternative assumptions about transmission route and spatial covariate data. Uncertainty in these modeling choices has generally not been accounted for in burden estimates and impact projections, meaning that uncertainty therein may be underrepresented. Collectively, these models span a range of assumptions, model structures, and inputs, any one of which can be viewed as reasonable and defensible. Several studies have modeled the probability of yellow fever occurrence (a binary outcome), but only a few have explicitly modeled its burden (a continuous outcome). After accounting for these factors to attain a model of disease burden, models can then be run under alternative scenarios about future vaccination to project its impact on the future burden of disease. Models offer the ability to extrapolate beyond known reports of yellow fever to account for underreporting, to account for the influence of vaccination coverage, demographic structure, and natural immunity on incidence patterns, and to leverage spatial patterns in data to inform geographically realistic estimates. In light of this complexity, modeling is an important tool for guiding vaccination policy for yellow fever. The global supply of yellow fever vaccine is also a limiting factor, given that outbreak response contributes to the depletion of vaccine stockpiles above and beyond use of the vaccine for routine immunization and supplementary immunization activities. Vaccinating the many people at risk of yellow fever on an ongoing basis is a challenge, however, given that areas where the virus occurs are geographically widespread and are inhabited by large populations with high birth rates. Thanks to safe and highly efficacious vaccines, yellow fever is vaccine-preventable in humans. Once infected, people experience a spectrum of disease severity, ranging from asymptomatic and mild infection to severe disease and death. The causative agent, yellow fever virus, is maintained in an enzootic cycle in non-human primates, and it infects humans primarily through spillover in communities in close proximity to sites of yellow fever epizootics in non-human primates. Yellow fever is a mosquito-borne viral disease that poses a risk to people throughout tropical areas of South America and Africa.
Combined with estimates that most infections go unreported (range of 95% credible intervals: 99.65-99.99%), our results suggest that yellow fever’s burden will remain highly uncertain without major improvements in surveillance. Even so, statistical uncertainty made even greater contributions to variance in burden estimates (87%). We addressed the latter with an ensemble approach, and we found that the former resulted in a nearly twentyfold difference in burden estimates (range of central estimates: 8.4×10 4-1.5×10 6 deaths in 2021-2030). We developed a framework for estimating the burden of yellow fever in Africa and evaluated its sensitivity to assumptions about the interpretation of serological data and choice of regression model.
Such estimates involve numerous assumptions, which uncertainty about is not always well accounted for.
Geographically stratified estimates of disease burden play an important role in setting priorities for the management of different diseases and for targeting interventions against a single disease.
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