{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T02:18:06Z","timestamp":1789870686556,"version":"4.0.1"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.24771.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000}}],"reference-count":57,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["226727_Z_22_Z"],"award-info":[{"award-number":["226727_Z_22_Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns3:p>Background Previous analytical work, defining the distribution of meningitis epidemics in Africa is over 20 years old, with climate change representing an ongoing issue. We aim to update this analysis and determine if the meningitis belt geography and associated environmental risk factors have changed in the last two decades. Methods Epidemic bacterial meningitis data from 2003\u20132022 were provided by WHO-AFRO. Districts across Africa were coded 1 if they experienced a meningitis outbreak and 0 if not. Monthly means of windspeed, rainfall, dust, and humidity were processed into climatic profiles using k-means clustering. We undertook logistic regression with meningitis epidemic history as the dependent variable and k-means clusters of rainfall, dust, humidity, and windspeed, alongside land-cover type as independent variables. A sensitivity analysis was conducted, excluding the Democratic Republic of Congo (DRC), due to limited laboratory confirmation of cases. Results Rainfall, dust, windspeed and humidity demonstrated the strongest statistical association with outbreaks and were included in our final model. With a probability cut-off &gt;0.4, our model had specificity and sensitivity of 82.07% and 82.22%, respectively, in identifying districts having experienced a meningitis epidemic. The Sahelian region had the highest risk of meningitis outbreaks (probability &gt;0.8), consistent with previous findings. The inclusion\/exclusion of the DRC had a significant impact on our model. In the full model the Republic of the Congo, Gabon, and Angola had a moderate risk of meningitis (probability &gt;0.4), suggesting a possible expansion of the belt. However, when the DRC was excluded, no countries surrounding the meningitis belt were at risk for outbreaks, highlighting the importance of laboratory testing and case confirmation. Conclusions The apparent extension of risk beyond the belt possibly reflects surveillance limitations rather than alterations in disease ecology. Where possible, laboratory confirmation should be used to support surveillance of suspected meningitis outbreaks and cases.<\/ns3:p>","DOI":"10.12688\/wellcomeopenres.24771.2","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T11:09:09Z","timestamp":1770980949000},"page":"497","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["New title: Re-evaluating environmental associations with meningitis risk across Africa"],"prefix":"10.12688","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1270-7536","authenticated-orcid":false,"given":"Molly","family":"Cliff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0408-9770","authenticated-orcid":false,"given":"Sally","family":"Jahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andre","family":"Bita Fouda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anderson","family":"Latt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clement","family":"Lingani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4000-2708","authenticated-orcid":false,"given":"Caroline","family":"Trotter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2012.0814","article-title":"Seasonality of meningitis in Africa and climate forcing: aerosols stand out.","volume":"10","author":"L Agier","year":"2013","journal-title":"J R Soc Interface."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"103-112","DOI":"10.1016\/j.ijid.2016.10.032","article-title":"Towards understanding the epidemiology of Neisseria meningitidis in the African meningitis belt: a multi-disciplinary overview.","volume":"54","author":"L Agier","year":"2017","journal-title":"Int J Infect Dis."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.214","article-title":"Present and future K\u00f6ppen-Geiger climate classification maps at 1\u2013km resolution.","volume":"5","author":"H Beck","year":"2018","journal-title":"Sci Data."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101797","article-title":"Meningococcal A conjugate vaccine coverage in the meningitis belt of Africa from 2010 to 2021: a modelling study.","volume":"56","author":"R Bender","year":"2023","journal-title":"EClinicalMedicine."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"807-815","DOI":"10.1023\/a:1007365919013","article-title":"Weather conditions and cerebrospinal meningitis in Benin (gulf of Guinea, West Africa).","volume":"13","author":"J Besancenot","year":"1997","journal-title":"Eur J Epidemiol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"461","DOI":"10.3390\/vaccines12050461","article-title":"The bacterial meningitis epidemic in Banalia in the Democratic Republic of Congo in 2021.","volume":"12","author":"A Bita Fouda","year":"2024","journal-title":"Vaccines (Basel)."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2022.107649","article-title":"Association between temperature variability and global meningitis incidence.","volume":"171","author":"J Chen","year":"2023","journal-title":"Environ Int."},{"key":"ref9","article-title":"Updating the meningitis belt: associations between environmental factors and epidemic meningitis risk across Africa-extended data and additional information.","author":"M Cliff","year":"2025b","journal-title":"figshare."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1101\/2025.02.25.25322867","article-title":"Re-examining the meningitis belt: associations between environmental factors and epidemic meningitis risk across Africa.","author":"M Cliff","year":"2025","journal-title":"medRxiv."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1515-1516","DOI":"10.1038\/s41591-024-02922-x","article-title":"Strategies for controlling pneumococcal disease and outbreaks during humanitarian emergencies.","volume":"30","author":"M Cliff","year":"2024","journal-title":"Nat Med."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"6923-6939","DOI":"10.3390\/ijerph110706923","article-title":"Climate change and cerebrospinal meningitis in the Ghanaian meningitis belt.","volume":"11","author":"S Codjoe","year":"2014","journal-title":"Int J Environ Res Public Health."},{"key":"ref13","doi-asserted-by":"publisher","article-title":"Agrometeorological indicators from 1979 to present derived from reanalysis.","year":"2020","DOI":"10.24381\/cds.6c68c9bb"},{"key":"ref14","doi-asserted-by":"publisher","article-title":"Aerosol properties gridded data from 1995 to present derived from satellite observation.","year":"2019","DOI":"10.24381\/cds.239d815c"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cliser.2022.100326","article-title":"Improved sub-seasonal forecasts to support preparedness action for meningitis outbreak in Africa.","volume":"28","author":"C Dione","year":"2022","journal-title":"Clim Serv."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"S434-S441","DOI":"10.1093\/cid\/civ551","article-title":"Introduction and rollout of a new group A meningococcal conjugate vaccine (PsA-TT) in African meningitis belt countries, 2010\u20132014.","volume":"61 Suppl 5","author":"M Djingarey","year":"2015","journal-title":"Clin Infect Dis."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"S174-S183","DOI":"10.1093\/infdis\/jiab105","article-title":"Pneumococcal meningitis outbreaks in Africa, 2000\u20132018: systematic literature review and meningitis surveillance database analyses.","volume":"224","author":"K Franklin","year":"2021","journal-title":"J Infect Dis."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.","volume":"2","author":"C Funk","year":"2015","journal-title":"Sci Data."},{"key":"ref19","article-title":"In world first, Nigeria introduces new 5-in-1 vaccine against meningitis","year":"2025"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"685-711","DOI":"10.1016\/S1474-4422(23)00195-3","article-title":"Global, regional, and national burden of meningitis and its aetiologies, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019.","volume":"22","year":"2023","journal-title":"Lancet Neurol."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"40-56","DOI":"10.1016\/j.apr.2019.09.009","article-title":"Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980\u20132019).","volume":"11","author":"P Govender","year":"2020","journal-title":"Atmos Pollut Res."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"341-353","DOI":"10.1016\/s0035-9203(99)90106-2","article-title":"Manson Lecture: meningococcal meningitis in Africa.","volume":"93","author":"B Greenwood","year":"1999","journal-title":"Trans R Soc Trop Med Hyg."},{"key":"ref23","first-page":"61-92","article-title":"The epidemiology of acute bacterial meningitis in tropical Africa","author":"B Greenwood","year":"1987"},{"key":"ref24","article-title":"Analysing, classifying and displaying time series of images pixel-by-pixel: the package ADDAPIX","author":"S Griguolo","year":"1996"},{"key":"ref25","article-title":"Generalized linear models and extensions, second edition.","author":"J Hardin","year":"2007"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24381\/cds.adbb2d47","article-title":"ERA5 hourly data on single levels from 1940 to present.","author":"H Hersbach","year":"2023"},{"key":"ref27","article-title":"raster: Geographic data analysis and modeling","author":"R Hijmans","year":"2025"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0019717","article-title":"Epidemiology, molecular characterization and antibiotic resistance of Neisseria meningitidis from patients \u226415 years in Manhi\u00e7a, rural Mozambique.","volume":"6","author":"A Ibarz-Pav\u00f3n","year":"2011","journal-title":"PLoS One."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/rs9010036","article-title":"Compilation and validation of SAR and optical data products for a complete and global map of Inland\/Ocean water tailored to the climate modeling community.","volume":"9","author":"C Lamarche","year":"2017","journal-title":"Remote Sens."},{"key":"ref30","first-page":"3-39","article-title":"La m\u00e9ningite c\u00e9r\u00e9bro-spinale en Afrique.","volume":"28","author":"L Lapeyssonnie","year":"1963","journal-title":"Bulletin of the World Health Organization."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"e34-e40","DOI":"10.1016\/S2542-5196(24)00308-5","article-title":"Sand and dust storms: a growing global health threat calls for international health studies to support policy action.","volume":"9","author":"T Li","year":"2025","journal-title":"Lancet Planet Health."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"S410-S415","DOI":"10.1093\/cid\/civ597","article-title":"Meningococcal meningitis surveillance in the African meningitis belt, 2004\u20132013.","volume":"61 Suppl 5","author":"C Lingani","year":"2015","journal-title":"Clin Infect Dis."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0008634","article-title":"The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa.","volume":"14","author":"S Mazamay","year":"2020","journal-title":"PLoS Negl Trop Dis."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-021-06724-1","article-title":"An overview of bacterial meningitis epidemics in Africa from 1928 to 2018 with a focus on epidemics \u201coutside-the-belt\u201d.","volume":"21","author":"S Mazamay","year":"2021","journal-title":"BMC Infect Dis."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1-6","DOI":"10.1080\/20477724.2016.1274068","article-title":"Emergence and control of epidemic meningococcal meningitis in sub-Saharan Africa.","volume":"111","author":"I Mohammed","year":"2017","journal-title":"Pathog Glob Health."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1287-93","DOI":"10.3201\/eid0910.030182","article-title":"Environmental risk and meningitis epidemics in Africa.","volume":"9","author":"A Molesworth","year":"2003","journal-title":"Emerg Infect Dis."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"242-249","DOI":"10.1016\/s0035-9203(02)90089-1","article-title":"Where is the meningitis belt? Defining an area at risk of epidemic meningitis in Africa.","volume":"96","author":"A Molesworth","year":"2002","journal-title":"Trans R Soc Trop Med Hyg."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"869-875","DOI":"10.1038\/s41558-022-01426-1","article-title":"Over half of known human pathogenic diseases can be aggravated by climate change.","volume":"12","author":"C Mora","year":"2022","journal-title":"Nat Clim Chang."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"70-77","DOI":"10.1016\/0035-9203(71)90187-8","article-title":"Cerebrospinal meningitis in East Africa 1911\u20131965.","volume":"65","author":"Y Mpairwe","year":"1971","journal-title":"Trans R Soc Trop Med Hyg."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"e553-559","DOI":"10.1016\/j.ijid.2009.08.013","article-title":"A hypothetical explanatory model for meningococcal meningitis in the African meningitis belt.","volume":"14","author":"J Mueller","year":"2010","journal-title":"Int J Infect Dis."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"114-127","DOI":"10.1016\/j.gloplacha.2017.12.014","article-title":"Rainfall over the African continent from the 19th through the 21st century.","volume":"165","author":"S Nicholson","year":"2018","journal-title":"Glob Planet Change."},{"key":"ref42","article-title":"Report: How climate change affects the human rights of Sahel region migrants","year":"2025"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.3402\/gha.v2i0.2061","article-title":"Meningococcal disease and climate.","volume":"2","author":"H Palmgren","year":"2009","journal-title":"Glob Health Action."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1047-1051","DOI":"10.1017\/S0950268806005929","article-title":"Evaluation of the meningitis epidemics risk model in Africa.","volume":"134","author":"E Savory","year":"2006","journal-title":"Epidemiol Infect."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1288-1294","DOI":"10.1016\/S1473-3099(16)30253-5","article-title":"Emergence of epidemic Neisseria meningitidis serogroup C in Niger, 2015: an analysis of national surveillance data.","volume":"16","author":"F Sidikou","year":"2016","journal-title":"Lancet Infect Dis."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"e6","DOI":"10.1371\/journal.pmed.0020006","article-title":"Climate drives the meningitis epidemics onset in West Africa.","volume":"2","author":"B Sultan","year":"2005","journal-title":"PLoS Med."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"715-724","DOI":"10.26502\/acmcr.96550549","article-title":"Impact of climate factors in modeling and predicting the transmission of meningitis in Africa: the case of Burkina Faso.","volume":"06","author":"H Tall","year":"2022","journal-title":"Arch Clin Med Case Rep."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"781-788","DOI":"10.1111\/j.1365-3156.2006.01630.x","article-title":"Potential of environmental models to predict meningitis epidemics in Africa.","volume":"11","author":"M Thomson","year":"2006","journal-title":"Trop Med Int Health."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"175-176","DOI":"10.1038\/d41586-020-02341-1","article-title":"Why deforestation and extinctions make pandemics more likely.","volume":"584","author":"J Tollefson","year":"2020","journal-title":"Nature."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"6212-6217","DOI":"10.1016\/j.vaccine.2015.09.107","article-title":"Response thresholds for epidemic meningitis in sub-Saharan Africa following the introduction of MenAfriVac\u00ae.","volume":"33","author":"C Trotter","year":"2015","journal-title":"Vaccine."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"867-872","DOI":"10.1016\/S1473-3099(17)30301-8","article-title":"Impact of MenAfriVac in nine countries of the African meningitis belt, 2010-15: an analysis of surveillance data.","volume":"17","author":"C Trotter","year":"2017","journal-title":"Lancet Infect Dis."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"323-9","DOI":"10.53854\/liim-3203-6","article-title":"A perspective on the novel pentavalent Men5CV (NmCV-5) meningitis vaccine and Nigeria\u2019s pioneering rollout campaign.","volume":"32","author":"B Ukoaka","year":"2024","journal-title":"Infez Med."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"121-129","DOI":"10.2991\/assehr.k.201010.019","article-title":"K-means clustering optimization using the elbow method and early centroid determination based on mean and median formula.","author":"E Umargono","year":"2020"},{"key":"ref54","article-title":"Pneumococcal meningitis \u2013 Togo","year":"2023"},{"key":"ref55","article-title":"In world first, Nigeria introduces new 5-in-1 vaccine against meningitis","year":"2024"},{"key":"ref56","article-title":"Meningococcal meningitis","year":"2025"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1186\/1476-072X-7-34","article-title":"Relationships between climate and year-to-year variability in meningitis outbreaks: a case study in Burkina Faso and Niger.","volume":"7","author":"P Yaka","year":"2008","journal-title":"Int J Health Geogr."},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2022.106213","article-title":"Analysis of spatiotemporal changes and driving factors of desertification in the Africa Sahel.","volume":"213","author":"Z Yang","year":"2022","journal-title":"CATENA."}],"container-title":["Wellcome Open Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T04:54:08Z","timestamp":1773291248000},"score":1,"resource":{"primary":{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,13]]},"references-count":57,"URL":"https:\/\/doi.org\/10.12688\/wellcomeopenres.24771.2","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/wellcomeopenres.24771.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.21956\/wellcomeopenres.28522.r147684","asserted-by":"subject"},{"id-type":"doi","id":"10.21956\/wellcomeopenres.28522.r147684","asserted-by":"object"}]},"ISSN":["2398-502X"],"issn-type":[{"value":"2398-502X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,13]]},"assertion":[{"value":"Indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.27291.r132782, MUHAMMAD UMAR AHSAN, D.G Khan Medical College, Punjab, 30 Sep 2025, version 1, 3 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v1#referee-response-132782","order":0,"name":"referee-response-132782","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Molly Cliff<\/b>; \n<i>Posted: 21 Jan 2026<\/i>; We would like to thank the reviewer for both taking the time to review this manuscript and for the insightful comments contained within the review. In this response we display the reviewer\u2019s comments in normal text and our response in italics  Is the work clearly and accurately presented, and does it cite the current literature?&nbsp;\n<b>Partly<\/b> Some important recent outbreak reports, WHO updates, and vaccination coverage studies are under-cited.&nbsp;  &nbsp; \n<i>We have updated the manuscript introduction to include details on the introduction of both MenA and Men5CV vaccines across the meningitis belt, citing key studies which also examine vaccination coverage. Additionally, we have included further details of recent bacterial meningitis outbreaks into the paper\u2019s introduction. If the reviewer still feels that we have under cited the literature, we would appreciate details of specific studies or reports that should be included. <\/i> &nbsp;  Is the study design appropriate and is the work technically sound?&nbsp;\n<b>Partly<\/b> The decision to exclude vaccination coverage and socio-demographic variables may oversimplify the model. Interpretation of a possible south-westerly expansion may reflect a data artifact.&nbsp;  \n<i>We have updated our analysis to include a sensitivity analysis which only considered outbreaks that occurred prior to the introduction of MenAfriVac on a country level. Details of this analysis can be found in both the results section of the manuscript as well as the extended data. Furthermore, within our discussion we examine how the inclusion of vaccination coverage and socio-demographic variables might alter the observed associations. One of the key challenges of analysing both vaccination coverage and socio-demographic variables is the availability of such data. It is unlikely that such data would be available of continental level at an appropriate resolution for inclusion within the model.<\/i> \n<i>Regarding the reviewer\u2019s comment that a possible south-westerly expansion may reflect a data artefact, we emphasize that this this a key point of the paper. Our analysis highlights that the possible expansion of the meningitis belt region is likely driven by unconfirmed cases of suspected meningitis &nbsp;in the <b>Democratic Republic of the Congo (DRC). Within this paper we argue that the apparent extension of the belt into the Republic of Congo, Gabon and Angola does not represent a climate driven shift in the meningitis belt but is instead likely an artefact of reporting practices in the DRC<\/b><b>,<\/b> with far fewer laboratory confirmed cases relative to neighbouring regions. <\/i>  Are sufficient details of methods and analysis provided to allow replication by others?\n<b>&nbsp;Partly&nbsp;<\/b> &nbsp;Parameter details for clustering (e.g., exact number of clusters chosen, initialization, random seed, convergence criteria) are not fully described.  \n<i>We have updated the methodology section of our paper to include parameter details for clusters, including number of clusters chosen, number of starts and convergence criteria. Alongside this our extended data includes random seeds and elbow plots for all variables, allowing for improved reproducibility. We have rerun the k-means clustering analysis increasing both the number of random starts and iterations from the default settings. In addition, district level outbreak weighting was subsequently adjusted to optimise sensitivity and specificity.<\/i>  &nbsp;If applicable, is the statistical analysis and its interpretation appropriate?&nbsp;\n<b>I cannot comment<\/b>. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility?&nbsp;\n<b>No<\/b> The environmental source data are open, but the epidemiological data and processed datasets are not accessible, which prevents full reproducibility.  \n<i>All datasets apart from the raw epidemiological surveillance data which is subject to data sharing restrictions from the World Health Organisation are publicly available. All processed datasets are available as part of the underlying data. <\/i> \n<i>Figshare: Updating the meningitis belt: associations between environmental factors and epidemic meningitis risk across Africa- Supporting data&nbsp;10.6084\/m9.figshare.29900975).<\/i> \n<i>Furthermore, within the manuscript we have documented all processing steps applied to the epidemiological data and made the R code publicly available on GitHub. <\/i> &nbsp;  Are the conclusions drawn adequately supported by the results?&nbsp;\n<b>Partly<\/b> Conclusions are partly supported by the results. The findings regarding rainfall, dust (AOD), and humidity as key environmental drivers, as well as the persistence of the Sahel as the primary high-risk zone, are demonstrated. However, the assertion of a south-westerly expansion of the meningitis belt (into Congo, Gabon, Liberia, and Angola) is less robust.&nbsp;  \n<i>Regarding the south-westerly expansion of the meningitis belt, as mentioned above this paper argues that this is likely an artefact of reporting practices within the Democratic of Congo. <b>Within the paper conclusion we advocate for the improvement of<\/b> epidemic reporting and diagnostic capacity within meningitis outbreaks, to accurately assess disease risk across the continent.<b> <\/b><\/i>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2#referee-comment-6388","order":1,"name":"referee-comment-6388","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.27291.r135924, Junjun Chen, Johns Hopkins University, Baltimore, 11 Nov 2025, version 1, 3 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v1#referee-response-135924","order":2,"name":"referee-response-135924","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Molly Cliff<\/b>; \n<i>Posted: 21 Jan 2026<\/i>; We would like to thank the reviewer for both taking the time to review this manuscript and for the insightful comments contained within the review. In this response we display the reviewer\u2019s comments in normal text and our response in italics &nbsp;  \n<b>Uncertainty communication<\/b> \n<b>Only point probabilities are mapped. For policy-relevant interpretation, users need a sense of uncertainty, especially in peripheral or moderate-risk regions. Consider adding uncertainty layers (eg, standard errors or 95% CIs of predicted probabilities) and\/or regional calibration plots to convey confidence in model outputs.<\/b>  \n<b><i>To enhance &nbsp;policy relevant interpretation we have added in risk maps with 95% CI have been added into the extended data, to convey confidence in model outputs.<\/i><\/b>  \n<b>2. Clustering reproducibility<\/b> \n<b>Clarify the k-means clustering setup and reproducibility parameters. While the workflow is outlined, exact specifications, such as the number of clusters per variable, selection criteria (elbow, silhouette, or SSE), initialization method, number of starts, stopping rule, and random seeds, should be reported. This transparency would greatly improve reproducibility.<\/b> \n<i>We have updated the methodology section of our paper to include parameter details for clusters, including number of clusters chosen, number of starts and convergence criteria. Alongside this our extended data includes random seeds and elbow plots for all variables, allowing for improved reproducibility. We have updated the k-means clustering analysis, increasing both the number of random starts and iterations from the default settings. Furthermore, district level outbreak weighting was adjusted to optimise sensitivity and specificity.<\/i>  \n<b>District mapping sensitivity<\/b> Potential misclassification may arise when mapping historical outbreaks to 2022 GADM boundaries using 50%\/30% overlap thresholds. A short sensitivity analysis varying these thresholds (eg, 40% and 60%) and reporting stability in coefficients or AUC would demonstrate robustness to boundary uncertainty.  &nbsp; \n<i>Within our methodology and results section, we have defined a sensitivity analysis that we undertook varying the overlap threshold of historical outbreaks to 40% and 60%. Within this analysis the AUC remained at 0.908, as only four district outbreaks were affected by the small boundary changes. <\/i> &nbsp;  \n<b>Data reproducibility<\/b> Because the WHO-AFRO outbreak data are not publicly accessible, full replication is currently limited. To improve transparency, consider releasing an anonymized proxy dataset (eg, district-level binary outcomes by country with masked IDs) or a deterministic script that reconstructs the analysis dataset from a standard WHO extract format. This would allow authorized users to fully reproduce the results.  \n<i>Unfortunately, it remains difficult to release an anonymized proxy version of the raw data, as the spatial identifiers within such a dataset would make it possible to identify the dates and locations of outbreaks. Furthermore, the outbreak dataset from WHO-AFRO is subject to explicit data usage and redistribution restrictions, preventing its release. <\/i> \n<i>To maximise reproducibility of results all datasets apart from the raw epidemiological surveillance data which is subject to data sharing restrictions from the World Health Organisation are publicly available. All processed datasets are available as part of the underlying data. <\/i> \n<i>Figshare: Updating the meningitis belt: associations between environmental factors and epidemic meningitis risk across Africa- Supporting data&nbsp;https:\/\/doi.org\/10.6084\/m9.figshare.29900975<\/i> \n<i>Furthermore, within the manuscript we have documented all processing steps applied to the epidemiological data and made the R code publicly available on GitHub. <\/i>  \n<b>5. Vaccination and immunity context<\/b> The exclusion of vaccination variables is reasonable to isolate environmental effects, but a brief sensitivity discussion on whether including simple covariates, such as MenAfriVac rollout year or PCV introduction by country, affects calibration or association strength would strengthen the interpretation. &nbsp; \n<i>We have updated our analysis to include a sensitivity analysis which only considered outbreaks that occurred prior to the introduction of MenAfriVac on a country level. Details of this analysis can be found in both the results section of the manuscript as well as the extended data. Furthermore, within our discussion we examine how the inclusion of vaccination coverage and socio-demographic variables might alter the observed associations.<\/i> \n<b>6. Recurrent-epidemic districts<\/b> It would be valuable to include a table of the top 20 districts with recurrent epidemics, along with their corresponding climate classes. This addition would enhance practical relevance for outbreak preparedness programs and country-level health planners. \n<i>The results section has now been updated to include a table of the top 20 districts with recurrent epidemics, along with their corresponding climate classes<\/i>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2#referee-comment-6389","order":3,"name":"referee-comment-6389","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.28522.r147684, Tiantian Li, Chinese Center for Disease Control and Prevention, Beijing, 12 Mar 2026, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-497\/v2#referee-response-147684","order":4,"name":"referee-response-147684","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by Wellcome [226727_Z_22_Z]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}