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Review 3: "Yellow Fever in Ghana: Predicting Emergence and Ecology from Historical Outbreaks"

While acknowledging the strengths of the studies, reviewers also offer constructive criticism regarding methodological clarity, data interpretation, and the need for updated references. 

Published onMar 08, 2024
Review 3: "Yellow Fever in Ghana: Predicting Emergence and Ecology from Historical Outbreaks"
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Yellow fever in Ghana: Predicting emergence and ecology from historical outbreaks
Yellow fever in Ghana: Predicting emergence and ecology from historical outbreaks

Abstract Understanding the epidemiology and ecology of yellow fever in endemic regions is critical for preventing future outbreaks. Ghana is a high-risk country for yellow fever. In this study we estimate the epidemiology, ecological cycles, and areas at risk for yellow fever in Ghana based on historical outbreaks. We identify 2371 cases and 887 deaths (case fatality rate 37.4%) from yellow fever reported in Ghana from 1910 to 2022. Since implementation of routine childhood vaccination in 1992, the estimated mean annual number of cases decreased by 81% and the geographic distribution of yellow fever cases also changed. While there have been multiple large historical outbreaks of yellow fever in Ghana from the urban cycle, recent outbreaks have originated among unvaccinated nomadic groups in rural areas with the sylvatic/savanna cycles. Using machine learning and an ecological niche modeling framework, we predict areas in Ghana that are similar to where prior yellow fever outbreaks have originated based on temperature, precipitation, landcover, elevation, and human population density. We find differences in predictions depending on the ecological cycles of outbreaks. Ultimately, these findings and methods could be used to inform further subnational risk assessments for yellow fever in Ghana and other high-risk countries.Author Summary Yellow fever is a viral hemorrhagic fever transmitted by mosquitoes in Africa and South America through different ecological transmission cycles. While West Africa has had the most cases of yellow fever, less is known about the epidemiology and ecology of yellow fever among countries in this region. Ghana has had multiple yellow fever outbreaks, including a recent outbreak in 2021-2022. In this study we estimate cases and deaths due to yellow fever in Ghana, compare the ecological cycles of outbreaks, and predict future areas at risk based on prior yellow fever cases and environmental conditions. We find that the populations at risk for yellow fever in Ghana have changed over the past century and that different ecological factors influence the risk of future emergence. Understanding these changes and the nuances of yellow fever epidemiology and ecology within countries will be important for future outbreak preparedness.

RR:C19 Evidence Scale rating by reviewer:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.


Review: Recurring outbreaks of Yellow Fever (YF) in Ghana are driven by a confluence of human and ecological factors and represent a high priority public health risk to Ghana and the region. Drawing on the historical record, the authors draw inferences about the conditions under which zoonotic outbreaks have occurred and identify regions at risk of future outbreaks. Subnational risk maps can inform public health policy, including efficient resource deployment and improved surveillance of both human populations and animal reservoirs.

This manuscript well-organized and clearly presented. Particular strengths include careful attention to local knowledge, a nuanced understanding of the statistical methods, and a thoughtful ecological analysis and interpretation of both the study system and the principle results. This study fills important gaps in the disease ecology of sub-Saharan Africa, a populous (and growing) area of the world that has received comparatively less public health funding and attention. 

There aren’t any major methodological issues present, but some notable weaknesses and a few areas which could benefit from clarity are as follows:

  • L224-5: It's not clear why species *richness* was chosen rather than NHP host density. Indeed, in some zoonotic systems, spillover seems to inversely correspond with species richness (ie rodents). The authors should clearly support their reasoning for their choice.

  • The authors first use the term "occurrence" in the methods, alongside "outbreaks" in Table 2 and methods. A clear delineation of how an occurrence *differs* from an outbreak would significant improve manuscript clarity. Related: including points showing occurrences on at least one of the maps (e.g. Fig 1) would help orient the reader. 

  • Lines 167-173 reference citation #23, which appears outdated and doesn't support many of the manuscript's assertions. A quick search turned up several good resources, such as Agwu et al. 2016 (Acta Tropica, Volume 161, p18-25).

Some minor issues:

  • Ref #24 is referred to repeatedly. A bit more exposition at first introduction re who/what/when would help contextualize results.

  • L198-201: Split into 2 sentences? Tough to parse as is.

  • L224-5: any literature on differential NHP host competence? Why not use presence of each species as a separate presence/absence? A bit more detail here would help readers with limited background in NHPs. 

  • L236: "a jackknife procedure" - a bit more detail here would be appreciated.

  • L243: "percent contribution was deemed less important" - by what measure? If it's not important, why include it?

  • L246-8: methods here are a bit tough to follow.

  • L297: explain "minimum training presence threshold" in text here (in addition to Fig caption). Does this differ from "minimum training presence cumulative threshold" (L250)? Related, a bit more narrative exposition on how Figs 3 and 4 are related would be appreciated (and possibly mentioned in respective caption(s)).

  • L295-313: results from multiple models are included in a long list. Consider breaking into shorter paragraphs and adding some transition text to orient the reader. 

  • L424-5: "AUC values can be inflated" - please cite.

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