Time series modelling and forecasting of the dynamics of infant mortalities in South Africa

Time series modelling and forecasting of the dynamics of infant mortalities in South Africa | 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 Time series modelling and forecasting of the dynamics of infant mortalities in South Africa Thembhani Hlayisani Chavalala, Happy Maluleke, Phillemon Dikgale This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7206270/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 Background Despite the significant progress made in child survival, there are still about 6500 daily newborn mortalities across the globe with sub-Saharan Africa, southern and central Asia bearing the heaviest burden. Prior studies frequently employed the Box-Jenkins framework to model and forecast infant mortality rates over time, although the daily infant deaths are count-based. The aim of this study was to evaluate and compare the performance of the traditional Box-Jenkins framework with a time series count-based process for modelling and forecasting the number of infant deaths in South Africa. Methods The Augmented Dickey-Fuller and Phillips-Perron tests revealed that the considered time series models should incorporate the trend and drift components. The ARIMA (2,0,1) with drift and trend components was fitted on the daily infant mortality data. Furthermore, the log-linear Poisson Autoregressive model that mimics the structure of an ARIMA (2,0,1) with drift and trend components was also considered for the same dataset. The residuals from both the fitted models were extracted and employed to assess how well the models fit the data. Key performance indicators were considered to determine which model provided the best fit for the data. Results Diagnostics tests confirmed that the two fitted models are adequate for the data. The Akaike Information Criterion and other model evaluation criteria’s demonstrate equivalent performance by the two models. For South Africa's data, both models revealed a negative trend coefficient, indicating a decline in infant mortality over time. Conclusion Model selection should be aligned with the characteristics of the data and analytical priorities. The log-linear Poisson autoregressive model is the most suitable framework for count-based time series like the daily number of infant deaths. The declining trend in infant mortality in South Africa suggests positive progress toward achieving Sustainable Development Goal 3.2, which targets ending preventable deaths of newborns and children under five. Infant mortality sub-Saharan Africa time series Box-Jenkins Poison autoregression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Editor invited by journal 29 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 24 Jul, 2025 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-7206270","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497913058,"identity":"c5dd3dec-e4a9-4a04-b244-72e28f44dbe3","order_by":0,"name":"Thembhani Hlayisani Chavalala","email":"data:image/png;base64,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","orcid":"","institution":"University of Limpopo","correspondingAuthor":true,"prefix":"","firstName":"Thembhani","middleName":"Hlayisani","lastName":"Chavalala","suffix":""},{"id":497913059,"identity":"5f9d5e9d-ae25-46d2-9188-99dfa1d4767f","order_by":1,"name":"Happy Maluleke","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Happy","middleName":"","lastName":"Maluleke","suffix":""},{"id":497913060,"identity":"57d763b6-4ef1-4b69-94bf-570a44dbcd8c","order_by":2,"name":"Phillemon Dikgale","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Phillemon","middleName":"","lastName":"Dikgale","suffix":""}],"badges":[],"createdAt":"2025-07-24 13:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7206270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7206270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88876912,"identity":"d2ccb886-ab31-49e6-b852-acbebfe6e239","added_by":"auto","created_at":"2025-08-12 10:20:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":455111,"visible":true,"origin":"","legend":"","description":"","filename":"modellingandforecastingofthedynamicsofinfantmortalitiesinSouthAfrica.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7206270/v1_covered_05db3564-80f4-4576-ae89-d63c5bea1499.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time series modelling and forecasting of the dynamics of infant mortalities in South Africa","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infant mortality, sub-Saharan Africa, time series, Box-Jenkins, Poison autoregression","lastPublishedDoi":"10.21203/rs.3.rs-7206270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7206270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDespite the significant progress made in child survival, there are still about 6500 daily newborn mortalities across the globe with sub-Saharan Africa, southern and central Asia bearing the heaviest burden. Prior studies frequently employed the Box-Jenkins framework to model and forecast infant mortality rates over time, although the daily infant deaths are count-based. The aim of this study was to evaluate and compare the performance of the traditional Box-Jenkins framework with a time series count-based process for modelling and forecasting the number of infant deaths in South Africa.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe Augmented Dickey-Fuller and Phillips-Perron tests revealed that the considered time series models should incorporate the trend and drift components. The ARIMA (2,0,1) with drift and trend components was fitted on the daily infant mortality data. Furthermore, the log-linear Poisson Autoregressive model that mimics the structure of an ARIMA (2,0,1) with drift and trend components was also considered for the same dataset. The residuals from both the fitted models were extracted and employed to assess how well the models fit the data. Key performance indicators were considered to determine which model provided the best fit for the data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDiagnostics tests confirmed that the two fitted models are adequate for the data. The Akaike Information Criterion and other model evaluation criteria\u0026rsquo;s demonstrate equivalent performance by the two models. For South Africa's data, both models revealed a negative trend coefficient, indicating a decline in infant mortality over time.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eModel selection should be aligned with the characteristics of the data and analytical priorities. The log-linear Poisson autoregressive model is the most suitable framework for count-based time series like the daily number of infant deaths. The declining trend in infant mortality in South Africa suggests positive progress toward achieving Sustainable Development Goal 3.2, which targets ending preventable deaths of newborns and children under five.\u003c/p\u003e","manuscriptTitle":"Time series modelling and forecasting of the dynamics of infant mortalities in South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 09:56:01","doi":"10.21203/rs.3.rs-7206270/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T04:25:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T08:25:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50049636904533614194438852192634627335","date":"2025-08-13T13:18:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265672790817491329359463975959626761846","date":"2025-08-08T06:34:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T19:41:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31200853768694129570037516160080462930","date":"2025-08-07T18:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210802351173920630312724635608504069686","date":"2025-08-07T15:28:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T00:52:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-29T07:12:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T10:33:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T10:31:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-07-24T13:45:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"683c40e3-4b34-49d9-93e6-a75db2dfe5fa","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-31T19:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 09:56:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7206270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7206270","identity":"rs-7206270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}