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Review 6: "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 23, 2024
Review 6: "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:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.


Review: This study identified modelled and mapped regions suitable for YF human case emergence, based on ecological cycles. Findings are aligned with locations of recent outbreaks that are mainly sylvatic and rural.

This paper aims to predict the habitats and factors associated with yellow fever virus emergence in Ghana. Using data from 1910-2022, the authors aim to explore the ecological cycles of YF in Ghana for the first time. Various ecological cycles (urban, sylvatic, and savanna) are identified and explored in this study which is identified as novel and a strength of this study. 

The authors use confirmed YF human cases, although it is not clear why certain years were excluded (e.g. what was the validation criteria?). Outbreaks are classified as urban, sylvatic, or savanna based on a selection of criteria related to mosquito reporting and environmental characteristics. The selection criteria for urban classification are well justified. However, it’s not clear what criteria was used for classifying sylvatic and savanna and how outbreaks are classified if characteristics make differentiation challenging. Only confirmed human cases of YF were included as input data. This raises a few questions: (1) Why were suspected cases not included? Given the study area and varying access to healthcare resources, I reckon that underreporting and sampling bias may be issues. (2) Why were mosquito data not included? The reporting of YF cases may be different than the ecological niche - where the virus emerged and where the mosquitos reside. Given that the authors aim to determine habitat suitability for pathogen spillover, this point is neglected in the analysis. 

From the confirmed cases, 21 occurrence points were identified. A Maxent ecological modelling technique is adopted to handle small sample modeling based on presence data only. There could be more discussion on potential issues predicting on a small sample set. Given there are more sophisticated model that could’ve been used in this case, further details are needed on why Maxent is the most appropriate model and how it addresses the limitations in the small sample. How many model iterations? Details needed here including 95% confidence interval. Further details and justifications are also needed the explanatory covariates used in the Maxent suitability model, specifically on years of data collection, and the type of bioclimatic variables. In particular, bioclimatic data are from 1970-2000 (more than two decades ago), which may not represent present day conditions especially when factoring in the effects of climate change. The authors should consider including present day data on environmental covariates which are available. 

In the results and discussion, the authors have addressed my concerns on why mosquito data were not included, given that the focus is on habitats for human cases. This requires clarity and should be emphasized at the start of the manuscript rather than later. This also doesn’t entirely “determine habitat suitability for pathogen spillover” which was claimed by the authors so this statement should be revised accordingly to reflect what is done in this study. Further, it is mentioned in the limitations discussion the earliest georeferenced cases were from 1960 (not 1910 as previously stated at the start). 

There is good reflection on methodological limitations. The interpretation of the findings is useful, specifically pertaining to the disparity in testing (south) vs cluster of YF emergence (north). The authors should consider creating a risk map based on these results; this would be useful for policymakers.

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