A decade of shifting cholera burden in Africa and its implications for control: a statistical mapping analysis

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1. 

   PREreview

   Oct 25, 2024

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/13993728.

   Does the introduction explain the objective of the research presented in the preprint? Partly The introduction is heavily focused on OCV, but the rest of the paper barely mentions it. The last paragraph should state clearly the goal of the research; what is the specific purpose of modeling the association between 2011-2020 cholera incidence and the spatial distribution of cholera in the post-2020 period.

   Are the methods well-suited for this research? Somewhat appropriate It is …

   This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/13993728.

   Does the introduction explain the objective of the research presented in the preprint? Partly The introduction is heavily focused on OCV, but the rest of the paper barely mentions it. The last paragraph should state clearly the goal of the research; what is the specific purpose of modeling the association between 2011-2020 cholera incidence and the spatial distribution of cholera in the post-2020 period.

   Are the methods well-suited for this research? Somewhat appropriate It is essential to clarify why these specific methods were chosen over simpler approaches, especially given that simpler methods might facilitate the communication of the results to policy-makers. We have concerns about the inconsistent definitions of suspected cholera cases across African countries, as this variability could compromise the reliability of the findings, we recommend to mention any efforts made to address this problem. Because we lack the expertise to fully assess the methods used, we recommend consulting an expert to evaluate the advantages and limitations of the methodology employed.

   Are the conclusions supported by the data? Somewhat supported The study provides detailed spatial analysis of cholera incidence across Africa, revealing patterns of disease burden and identifying high-incidence areas. Data inconsistencies such as changes in surveillance and vague definition of suspected cases were mentioned, but not thoroughly explained. ''The data extracted corresponded primarily to cholera reported between January 2022 and December 2023, with limited additional data in the surrounding months due to temporal resolution of reports. For this analysis, locations were determined to have cholera if one or more suspected cholera cases were reported in any of the status reports described above. We then spatially linked the extracted locations to the set of ADM2 units used to summarize the cholera incidence mapping results (SM - Cholera Incidence Analysis 2022-2023).'' The limited data has a justification that is raised in the paper, however an expert is required to assess whether this method adds something different compared to the conventional ones. ''Suspected cholera case definitions varied by data source and were oftentimes not stated, but were commonly variations of the recommended WHO suspected case definition, such as "any patient presenting with or dying from acute watery diarrhea" and "a patient aged 2 years or more develops acute watery diarrhea with or without vomiting." This statement use some variants of the suspected cholera case definition and give examples of these, but do not fully systematize the definitions, which may lead to biases that are not considered a limitation.

   Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear The maps are the strongest part of the document. They effectively communicate the main findings by using colors to reflect the incidence of cholera by region. Moreover, they allow readers to visually interpret shifts in cholera burden over time, enhancing the understanding of temporal trends in the continent.

   How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly The authors provide a thoughtful discussion, explanation, and interpretation of their findings, as well as potential next steps. They effectively explain the temporal and geographic distribution of cholera burden in Africa through maps, highlighting geographic shifts and concluding that cases remain spatially concentrated. They also emphasize the need for specific targeted interventions to reduce the burden of infections. However, it is important for the authors to identify specific actions regarding the "infrastructure investments" mentioned in the discussion. They should justify these recommendations with supporting data and assess their implementation feasibility. Additionally, it would be essential for the authors to address how they accounted for the fact that V. cholerae cases may account on average for only half of suspected cases as a potential confounding factor.

   Is the preprint likely to advance academic knowledge? Moderately likely We find value in this model's potential to be beneficial for interventions aimed at preventing cholera based on patterns in geographic areas across Africa. Nevertheless, it is important to characterize the significance of this method compared to more conventional approaches. Moreover, the preprint does not compare this methodological approach with traditional methods, we recommend conducting a comparative analysis with other models to evaluate the evidence and impact of the results.

   Would it benefit from language editing? Yes The language can be confusing for readers without a similar research background, so it would be appropriate to rephrase the next sentence: 'We develop a hierarchical Bayesian modeling framework that considers the spatiotemporal heterogeneity of suspected cholera incidence. This framework also accounts for the variability and overlap in the spatial and temporal scales of case reports from different data sources.' In this sentence, we suggest breaking down the information and reorganizing it to enhance understanding. E.g 'We have created a hierarchical Bayesian modeling framework that takes into account the varying patterns of suspected cholera incidence over time (time periods) and space (countries). This framework looks at how the timing and locations of case reports from various data sources are different and similar.'

   Would you recommend this preprint to others? Yes, but it needs to be improved We would recommend this preprint with reservations. It offers valuable insights into cholera epidemiology in Africa, which could be useful for experts in the field and those familiar with the methods employed. However, the complex methods may be challenging to assess and understand for readers without a strong statistical background to fully appreciate its contributions.

   Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes It needs detailed explanations of methods and corrections of the observations mentioned. It is of crucial importance that someone with expertise in the methods be consulted as peer reviewer, specifically in spatio-temporal analysis of infectious diseases, particularly in enteric pathogens.

   Competing interests

   The authors declare that they have no competing interests.

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2. 

   Version published to 10.1101/2024.09.23.24314072v1 on medRxiv

   Sep 24, 2024