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Review 4: "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 4: "Yellow Fever in Ghana: Predicting Emergence and Ecology from Historical Outbreaks"
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key-enterThis Pub is a Review of
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: This preprint can be summarized as: 1. Despite the frequency of cases of yellow fever, there are few studies that evaluate the characteristics of human cases; 2. the dynamic of cases in Ghana have shifted, largely attributed to human decisions based on vaccination adherence; 3. Habitat suitability models of DF are strongly predicted, but need to be further assessed to include a lot of other important information pertaining to the sylvatic and savannah cycles.

Overall, this was a very well written preprint that would make an excellent contribution to a much-needed, and neglected, disease system. There are lots of assumptions made with notable limitations, but the authors provided these clearly and honestly. Any habitat suitability model will have numerous choices for inputs and corresponding inclusion criteria – but this is highly informative and makes a strong case for several, key points that are important for taking the steps necessary to reduce the burden of YF in Ghana.

Smaller points to consider:

  • Yellow Fever is unique in that other non-human primates serve in the sylvatic cycle, and sometimes, with humans, contribute collectively to the transmission cycle in the savannah/intermediate cycle. Has this been considered in the modeling? Non-human primate surveillance and/or density maps (species richness is good for presence/absence, as you have used, but something relating to the abundance of the animals of interest would be even better) would also be interesting to include as additional predictors for Aedes/Yellow Fever transmission spots.

  • Similarly, are there any surveillance or abundance records of Aedes sp. Mosquitoes? E.g. “hot spots” of mosquito activity?

  • Is there information/surveys on vaccine adherence within Ghana? Similarly to the above point, would be nice to have that data to include in models.

  • I wonder how far people travel to get to healthcare facilities where they were evaluated – good to consider when using that point as where case was acquired (even though we know it wasn’t).

  • The resolution is unclear for how locations were determined for cases. This should be elaborated for further clarity (however, Table 1 is ideal, but I don’t see information on the dependent variable(s)). It appears that given the authors removed region or district level cases, this must be estimated point data?

  • I would be curious to look into the density of cases – if possible, it may be highly likely that cases within 1km of each other are in fact, legitimate cases (especially if the date reported is different). This could then be an added weighted predictor, as you’ll have >1 case per 1km2, for example. However, I imagine the data is not detailed enough to be certain near cases aren’t duplicates?

  • Were the house index, container index, and Breteau index collected by this group, for this study? Or is this previously collected, public data that was integrated (and if so, what time period)?

  • Table 1 is a great summary table (nice to see how the inputs are categorized) – it’s now clear that this will be a habitat suitability model. Given that, have the authors’ maximums, hindering the mosquito’s distribution)? That is why I asked earlier about surveillance data – that would help with this as well.

  • It would be nice to see the collinearity table matrix (I’m curious what covariates were strongly correlated)

  • I think Table 2 is incredible – a lot of excellent information!

  • What software were the statistics/modeling conducted? I don’t recall seeing this (other than some details on ArcGIS Pro for the collinearity assessment).

  • Overall, the changes in the ecologies and distribution of YF seem to be mostly around vaccinations (human behavior). I hope the authors continue this and incorporate surveys or estimates of vaccine adherence, or at minimum, % vaccinated at the highest resolutions possible, then re-run these analyses. I think that would streamline the targeted locations and make a big public health difference.

  • Also, so many cases are in the urban cycle – is there any mitigation or control efforts? What about education campaigns? If people eliminated (or covered) suitable habitats, especially artificial containers, this could make a big difference.

  • I noticed that in Figure 3, the overall YF model is showing the influence on case occurrences within geopolitical borders. The suitability for district “Savannah” is clearly delineated from the surrounding regions, particularly “Northern”. This is an artifact of the model inputs – ideally, these political boundaries are smoothed or incorporated in the analyses to produce pixel transitions, so there is less of that influence. But this is a small point in an otherwise fantastic manuscript.

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