Quantifying model evidence for yellow fever transmission routes in Africa

• Published in: PLoS computational biology 2019-09, Vol.15 (9), p.e1007355
• Main Authors: Gaythorpe, Katy A M, Jean, Kévin, Cibrelus, Laurence, Garske, Tini
• Format: Article
• Language: English
• Subjects: Aedes - virology; Africa; Animals; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Computational Biology; Computer simulation; Disease transmission; Distribution; Epidemics; Estimates; Fever; Human health and pathology; Humans; Infections; Infectious diseases; Life Sciences; Markov analysis; Markov chains; Markov processes; Mathematical models; Medicine and Health Sciences; Models, Biological; Models, Statistical; Monte Carlo methods; Mosquitoes; Parameter estimation; Parameter uncertainty; People and Places; Physical Sciences; Predictions; Public health; Quantitative psychology; Reservoirs (Water); Robustness (mathematics); Santé publique et épidémiologie; Serological surveys; Tropical diseases; Tropical environment; Tropical environments; Vector-borne diseases; Yellow fever; Yellow Fever - epidemiology; Yellow Fever - transmission; Yellow fever virus
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007355

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model's assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model's evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to comp