Forecasting Tuberculosis Incidence in Somalia: A Comparative Analysis of Single and Hybrid Time-Series Models

Forecasting Tuberculosis Incidence in Somalia: A Comparative Analysis of Single and Hybrid Time-Series Models | 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 Forecasting Tuberculosis Incidence in Somalia: A Comparative Analysis of Single and Hybrid Time-Series Models Hana Mahdi Dahir, Ayan Husein Korse, Saralees Nadarajah, Farduus Ibraahim Mohamed, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7727080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tuberculosis (TB) remains a significant public health challenge, necessitating accurate forecasting methodologies for effective control and prevention strategies. This paper explores the application of hybrid models for forecasting TB incidence in Somalia. The study employs a comprehensive suite of 14-time series models, including five single models—ARIMA (Autoregressive Integrated Moving Average), ETS (Error Trend Seasonality), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal), Theta, and NNAR (Neural Network Auto Regression)—and nine hybrid models (ARIMA-ETS, ARIMA-NNAR, ARIMA-Theta, ARIMA-TBATS, ARIMA-ETS-Theta, ARIMA-ETS-NNAR, ARIMA-ETS-TBATS, ARIMA-Theta-NNAR, and ARIMA-TBATS-NNAR). Annual TB incidence data from 2000 to 2022, sourced from the World Bank, is utilized to train and evaluate the models. Model performance is assessed using metrics such as Theil's U statistic, Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). The TBATS model demonstrates the best fit among the single time series models, while the ARIMA-ETS-TBATS hybrid model outperforms the other hybrid models. The resulting forecasts provide valuable insights into the future TB incidence trends in Somalia, aiding in informed public health decision-making and targeted intervention strategies. The study underscores the importance of hybrid modeling for enhanced forecasting accuracy in the context of TB control efforts. Tuberculosis incidence Hybrid models Somalia Time-series models Forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7727080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532808760,"identity":"d7607908-2e67-43e0-bc82-2955dfda7615","order_by":0,"name":"Hana Mahdi Dahir","email":"","orcid":"","institution":"Amoud University","correspondingAuthor":false,"prefix":"","firstName":"Hana","middleName":"Mahdi","lastName":"Dahir","suffix":""},{"id":532808762,"identity":"3d4e9b97-9e27-4a9a-8431-25e95bb77520","order_by":1,"name":"Ayan Husein 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Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Tuberculosis incidence, Hybrid models, Somalia, Time-series models, Forecasting","lastPublishedDoi":"10.21203/rs.3.rs-7727080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7727080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuberculosis (TB) remains a significant public health challenge, necessitating accurate forecasting methodologies for effective control and prevention strategies. This paper explores the application of hybrid models for forecasting TB incidence in Somalia. The study employs a comprehensive suite of 14-time series models, including five single models\u0026mdash;ARIMA (Autoregressive Integrated Moving Average), ETS (Error Trend Seasonality), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal), Theta, and NNAR (Neural Network Auto Regression)\u0026mdash;and nine hybrid models (ARIMA-ETS, ARIMA-NNAR, ARIMA-Theta, ARIMA-TBATS, ARIMA-ETS-Theta, ARIMA-ETS-NNAR, ARIMA-ETS-TBATS, ARIMA-Theta-NNAR, and ARIMA-TBATS-NNAR). Annual TB incidence data from 2000 to 2022, sourced from the World Bank, is utilized to train and evaluate the models. Model performance is assessed using metrics such as Theil's U statistic, Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). The TBATS model demonstrates the best fit among the single time series models, while the ARIMA-ETS-TBATS hybrid model outperforms the other hybrid models. The resulting forecasts provide valuable insights into the future TB incidence trends in Somalia, aiding in informed public health decision-making and targeted intervention strategies. 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