Conflict and displacement as drivers of first‑dose measles vaccine coverage in Somalia: An ecological analysis

Conflict Hotspot Trajectories in Somalia from 2015-2023: Descriptive Spatial Diagnostics to Inform Measles Program Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Conflict Hotspot Trajectories in Somalia from 2015-2023: Descriptive Spatial Diagnostics to Inform Measles Program Monitoring John Kwame Duah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7235155/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background Conflict in Somalia is geographically uneven, with persistent concentration in specific areas that challenge humanitarian access and routine health service delivery. Program monitoring and situational awareness often rely on national summaries that mask subnational patterns. Descriptive spatial diagnostics provide a transparent approach to documenting where and to what extent conflict clusters evolve over time, supporting planning without requiring causal assumptions. Methods A descriptive spatial analysis was conducted for 2015-2023 using geocoded events from the Armed Conflict Location and Event Data (ACLED) dataset, aggregated to GADM v3.6 administrative level-2 (district) polygons. Violent incident types retained were Battles, Explosions/Remote violence, and Violence against civilians. Global spatial autocorrelation was assessed annually with Moran’s I using queen contiguity. Local clustering was identified with the Local Getis-Ord Gi* statistic; districts with z-scores > 1.96 were classified as hot spots and z < −1.96 as cold spots. Annual hotspot prevalence was defined as the percentage of districts classified as hotspots. Results Conflict showed significant spatial clustering each year (2015: Moran’s I = 0.27, z = 4.61, p < 0.001; 2023: Moran’s I = 0.33, z = 5.50, p < 0.001). The share of districts classified as hotspots increased from 5.4% in 2015 to a peak of 10.8% in 2019 and was 10.8% in 2023. Multi-year hotspot maps illustrate persistent concentration in specific areas rather than dispersed patterns . Conclusions Descriptive spatial diagnostics offer a reproducible approach to detecting and tracking conflict clustering in Somalia, revealing persistent geographic concentration from 2015-2023. These findings support sub-national monitoring and planning without relying on causal assumptions. All code and data sources are documented in Supplement A to facilitate transparency and reuse. Somalia conflict hotspots humanitarian health spatial autocorrelation ACLED Moran’s I Getis-Ord Gi* Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryDiagnosticChartclean.pdf somaliaanalyticcode.R.docx supplementAoptionA.pdf Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7235155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501822259,"identity":"989701d5-882e-449b-bd92-091c37cf4461","order_by":0,"name":"John Kwame 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Monitoring\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Somalia, conflict hotspots, humanitarian health, spatial autocorrelation, ACLED, Moran’s I, Getis-Ord Gi*","lastPublishedDoi":"10.21203/rs.3.rs-7235155/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7235155/v2","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\u003eConflict in Somalia is geographically uneven, with persistent concentration in specific areas that challenge humanitarian access and routine health service delivery. Program monitoring and situational awareness often rely on national summaries that mask subnational patterns. Descriptive spatial diagnostics provide a transparent approach to documenting where and to what extent conflict clusters evolve over time, supporting planning without requiring causal assumptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA descriptive spatial analysis was conducted for 2015-2023 using geocoded events from the Armed Conflict Location and Event Data (ACLED) dataset, aggregated to GADM v3.6 administrative level-2 (district) polygons. Violent incident types retained were Battles, Explosions/Remote violence, and Violence against civilians. Global spatial autocorrelation was assessed annually with Moran’s I using queen contiguity. Local clustering was identified with the Local Getis-Ord Gi* statistic; districts with z-scores \u0026gt; 1.96 were classified as hot spots and z \u0026lt; −1.96 as cold spots. Annual hotspot prevalence was defined as the percentage of districts classified as hotspots.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict showed significant spatial clustering each year (2015: Moran’s I = 0.27, z = 4.61, p \u0026lt; 0.001; 2023: Moran’s I = 0.33, z = 5.50, p \u0026lt; 0.001). The share of districts classified as hotspots increased from 5.4% in 2015 to a peak of 10.8% in 2019 and was 10.8% in 2023. Multi-year hotspot maps illustrate persistent concentration in specific areas rather than dispersed patterns\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive spatial diagnostics offer a reproducible approach to detecting and tracking conflict clustering in Somalia, revealing persistent geographic concentration from 2015-2023. These findings support sub-national monitoring and planning without relying on causal assumptions. 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