Wavelet analysis and time series forecasting of malaria incidence acrossdifferent transmission settings in Nigeria

Wavelet analysis and time series forecasting of malaria incidence acrossdifferent transmission settings in Nigeria | 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 Wavelet analysis and time series forecasting of malaria incidence acrossdifferent transmission settings in Nigeria Emmanuel Afolabi BAKARE, Deborah Oluwatobi Daniel, Samuel Abidemi Osikoya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8544922/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract \noindent Malaria remains a public health challenge in Nigeria, characterised by high endemicity with seasonal and perennial transmission, varying between regions. Existing control programmes often face challenges related to the allocation of resources and the timing of interventions, which may be due to an inadequate understanding of local seasonal transmission patterns. Hence, this study analysed the temporal patterns of malaria incidence in Bauchi, Yobe, and Ogun states (representing high, low, and moderate transmission settings) to identify hidden patterns in non-stationary time series and forecast trends for targeted interventions. Using time series, Wavelet, and SARIMA methods, strong annual peaks in Bauchi and Yobe during September/October and stable transmission in Ogun with peaks in July were identified. Wavelet analysis identified a dominant 12-month periodicity in all selected states, strong synchrony between Bauchi and Yobe, with Bauchi leading approximately a week. On the other hand, Ogun is observed to lead the northern states (Bauchi and Yobe) by approximately 2 months. The SARIMA models predicted persistent cyclical patterns into 2026, with monthly cases of 52,000-125,000 in Bauchi, 54,000-185,000 in Yobe, and 20,000-32,000 in Ogun, peaking in October for Bauchi/Yobe and July for Ogun. This study recommends harmonised Seasonal Malaria Chemoprevention (SMC) campaigns in Bauchi and Yobe beginning before September to maximise protection during peak transmission, mass distribution of long-lasting insecticide net (LLIN) in Ogun by May / June to precede peak transmission in July, and the use of predictions as an early warning system for health resource planning, improving data-driven control strategies for the National Malaria Elimination Programme (NMEP). Malaria Time Series SARIMA Forecasting Wavelet Analysis Nigeria Epidemiology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 20 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 07 Jan, 2026 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. 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-8544922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578296942,"identity":"8a8d98f1-9af7-4cd8-ba03-cfc55364dfa4","order_by":0,"name":"Emmanuel Afolabi 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Existing control programmes often face challenges related to the allocation of resources and the timing of interventions, which may be due to an inadequate understanding of local seasonal transmission patterns. Hence, this study analysed the temporal patterns of malaria incidence in Bauchi, Yobe, and Ogun states (representing high, low, and moderate transmission settings) to identify hidden patterns in non-stationary time series and forecast trends for targeted interventions. Using time series, Wavelet, and SARIMA methods, strong annual peaks in Bauchi and Yobe during September/October and stable transmission in Ogun with peaks in July were identified. Wavelet analysis identified a dominant 12-month periodicity in all selected states, strong synchrony between Bauchi and Yobe, with Bauchi leading approximately a week. On the other hand, Ogun is observed to lead the northern states (Bauchi and Yobe) by approximately 2 months. The SARIMA models predicted persistent cyclical patterns into 2026, with monthly cases of 52,000-125,000 in Bauchi, 54,000-185,000 in Yobe, and 20,000-32,000 in Ogun, peaking in October for Bauchi/Yobe and July for Ogun. This study recommends harmonised Seasonal Malaria Chemoprevention (SMC) campaigns in Bauchi and Yobe beginning before September to maximise protection during peak transmission, mass distribution of long-lasting insecticide net (LLIN) in Ogun by May / June to precede peak transmission in July, and the use of predictions as an early warning system for health resource planning, improving data-driven control strategies for the National Malaria Elimination Programme (NMEP).","manuscriptTitle":"Wavelet analysis and time series forecasting of malaria incidence acrossdifferent transmission settings in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 18:27:14","doi":"10.21203/rs.3.rs-8544922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-11T07:36:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T10:03:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T17:03:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225138190963187364612213870050353808747","date":"2026-02-06T14:47:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176890948767170412679815149027534117878","date":"2026-02-06T08:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263283374415548967691919102455849651529","date":"2026-02-06T07:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229822382592847665356282808909700300396","date":"2026-02-01T07:03:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T07:57:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T05:20:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T10:10:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T10:08:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-01-07T19:52:57+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"1d4a2591-fedd-41b1-8ca7-c02fb9d74797","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T03:38:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 18:27:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8544922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8544922","identity":"rs-8544922","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}