Bayesian spatiotemporal modelling and mapping of malaria risk among children aged below 5 years in Ghana

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Aheto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4361438/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 3 You are reading this latest preprint version Abstract Background Malaria is a significant public health problem, particularly among children aged 6-59 months who bear the greatest burden of this disease. Malaria transmission is high and more pronounced in poor tropical and subtropical areas of the world. Climate change is positively correlated with the geographical distribution of malaria vectors. There is substantial evidence of spatial and temporal differences in under-five malaria risk. Thus, the study aimed to create intelligent maps of smooth relative risk of malaria in children under-5 years that highlights high and low malaria burden in space and time to support malaria prevention, control, and elimination efforts. Method The study extracted and merged the required data on malaria among children aged 6-59 months from 2014 Ghana Demographic and Health Surveys (GDHS), 2016 and 2019 Ghana Malaria Indicator Surveys (GMIS). The outcome variable of interest is the count of children aged 6-59 months with positive test on rapid diagnostic test (RDT) kit. Bayesian Hierarchical Spatiotemporal models were specified to estimate and map spatiotemporal variations in the relative risk of malaria. The existence of local clustering was assessed using local indicator of spatial association (LISA) and the points were mapped to display significant local clusters, hotpot, and cold spot communities. Results The number of positive malaria cases in children aged 6-59 months decreased marginally between the 2014 and 2019 DHS survey periods. Smooth relative risk of malaria among children aged 6-59 months has consistently increased in the Northern and Eastern regions between 2014 and 2019. Socioeconomic and climatic factors such as household size [Posterior Mean: -0.198 (95% CrI: 3.52, 80.95)], rural area [Posterior Mean: 1.739 (95% CrI: 0.581, 2.867)], rainfall [Posterior Mean: 0.003 (95% CrI: 0.001, 0.005)], and maximum temperature [Posterior Mean: -1.069 (95% CrI: -2.135, -0.009)] have all been shown as statistically significant predictors of malaria risk in children aged 6-59 months. Hot spot DHS clusters with a significantly high relative risk of malaria among children aged 6-59 months were repeatedly detected in the Ashanti region between 2014 and 2019. Conclusion The findings would provide policymakers with practical and insightful information for the equitable distribution of scarce health resources targeted at reducing the burden of malaria and its associated mortality among children under-five years. Malaria Mathematical modeling spatiotemporal modeling Ghana Children under-five years Figures Figure 1 Figure 2 Figure 3 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editor assigned by journal 10 May, 2024 Submission checks completed at journal 08 May, 2024 First submitted to journal 02 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4361438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301009647,"identity":"a06488e6-9edf-4ecd-b9db-8dacfa2a5f71","order_by":0,"name":"Wisdom Kwami Takramah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFACNjCZwM/AA2YwNsCEcAEemBbJBpK1GBwgVos9+7G0Dz9z7PKMb+Qe/PiDwUZ2wwH2aw/w2sKTdnhm77bkYrMbecnSPAxpxhsO8JQb4HdYejMD7zbmxG03cgykGRgOJwK1pEng1cL/vJnx77b6xM0zcox//mD4T4QWibTDzLzbgIZL5JhJ8DAcAGphP4Zfy41nycyy244nzjjzxsyaxyDZeOZhHja8Wtj704wZ326rTuxvzzG++aPCTrbvePszvFrQACiomHnwBhh2mx+QrGUUjIJRMAqGNQAAbnFJ8cn2y40AAAAASUVORK5CYII=","orcid":"","institution":"University of Health and Allied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Wisdom","middleName":"Kwami","lastName":"Takramah","suffix":""},{"id":301009648,"identity":"ebc8e00a-dc00-46df-a882-1314b7095238","order_by":1,"name":"Yaw Asare Afrane","email":"","orcid":"","institution":"University of Ghana Medical School","correspondingAuthor":false,"prefix":"","firstName":"Yaw","middleName":"Asare","lastName":"Afrane","suffix":""},{"id":301009649,"identity":"876f0bd9-0de8-49f4-ba55-68694350c79d","order_by":2,"name":"Justice Moses K. Aheto","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Justice","middleName":"Moses K.","lastName":"Aheto","suffix":""}],"badges":[],"createdAt":"2024-05-03 01:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4361438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4361438/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-10787-9","type":"published","date":"2025-03-28T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56619673,"identity":"0fca312e-87f9-455b-ab2f-5ca8e2ee15d2","added_by":"auto","created_at":"2024-05-16 17:49:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257150,"visible":true,"origin":"","legend":"\u003cp\u003eRegional distribution of malaria prevalence (%) among children aged 6-59 months who tested positive for malaria by rapid diagnostic test (RDT) in 2014 (top left), 2016 (top right) and 2019 (bottom left). Note: The figures inserted in the maps are the percentage of children who tested positive for malaria. The same scale was used for the three survey years in the maps to allow for better comparison across the three survey years.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4361438/v1/b9dc9df1c1a72fd8ffc70069.jpg"},{"id":56619674,"identity":"fea4439e-dfa5-4eaf-8823-665e75bef013","added_by":"auto","created_at":"2024-05-16 17:49:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2487201,"visible":true,"origin":"","legend":"\u003cp\u003eSmooth relative risk of malaria among children aged 6-59 months according to RDT obtained from weighted Bayesian Hierarchical Spatiotemporal Model with covariate.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4361438/v1/bf55740bb5ba224066f96131.jpg"},{"id":56619675,"identity":"85deac81-7b1c-4ba6-946a-c77a6b5169d3","added_by":"auto","created_at":"2024-05-16 17:49:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":410021,"visible":true,"origin":"","legend":"\u003cp\u003eLISA Cluster map showing statistically significant relative risk of malaria among childing aged 6-59 months according to RDT across GDHS clusters in Ghana obtained from geostatistical model, 2014-2019.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4361438/v1/cce4d325430f5fa290566060.jpg"},{"id":79604823,"identity":"068c165a-d873-43a1-9795-0104a0df706b","added_by":"auto","created_at":"2025-03-31 16:07:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3682411,"visible":true,"origin":"","legend":"","description":"","filename":"ModelingofSpatiotemporalMalariaTransmissionWTKV6JAClean1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4361438/v1_covered_cf6b939f-210b-4bfa-a6d3-4dc0a7a88167.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian spatiotemporal modelling and mapping of malaria risk among children aged below 5 years in Ghana","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Mathematical modeling, spatiotemporal modeling, Ghana, Children under-five years","lastPublishedDoi":"10.21203/rs.3.rs-4361438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4361438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalaria is a significant public health problem, particularly among children aged 6-59 months who bear the greatest burden of this disease. Malaria transmission is high and more pronounced in poor tropical and subtropical areas of the world. Climate change is positively correlated with the geographical distribution of malaria vectors. There is substantial evidence of spatial and temporal differences in under-five malaria risk. Thus, the study aimed to create intelligent maps of smooth relative risk of malaria in children under-5 years that highlights high and low malaria burden in space and time to support malaria prevention, control, and elimination efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study extracted and merged the required data on malaria among children aged 6-59 months from 2014 Ghana Demographic and Health Surveys (GDHS), 2016 and 2019 Ghana Malaria Indicator Surveys (GMIS). The outcome variable of interest is the count of children aged 6-59 months with positive test on rapid diagnostic test (RDT) kit. Bayesian Hierarchical Spatiotemporal models were specified to estimate and map spatiotemporal variations in the relative risk of malaria. The existence of local clustering was assessed using local indicator of spatial association (LISA) and the points were mapped to display significant local clusters, hotpot, and cold spot communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of positive malaria cases in children aged 6-59 months decreased marginally between the 2014 and 2019 DHS survey periods. 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