Estimating the Spread Scenario of Acute Diarrhea and Dengue Diseases in India: A Comparative Analysis of Statistical, Mathematical, and Deep Learning Models

Estimating the Spread Scenario of Acute Diarrhea and Dengue Diseases in India: A Comparative Analysis of Statistical, Mathematical, and Deep Learning 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 Article Estimating the Spread Scenario of Acute Diarrhea and Dengue Diseases in India: A Comparative Analysis of Statistical, Mathematical, and Deep Learning Models Avaneesh Singh, Manish Kumar Bajpai, Krishna Kumar Sharma, Kailash Wamanrao Kalare, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4766588/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This work estimates the spread scenario of acute diarrhoea and dengue diseases in India. Statistical, mathematical (compartmental), and deep learning time series models are employed to forecast the spread of these diseases. Predictions are based on the reported cases and fatalities recorded between January 1, 2011, and December 31, 2021. Ten different techniques for disease spread forecast, including Regression, Bayesian Linear Regression MultiOutputRegressor + XGBoost, SIR model, Prophet, NBEATS, Gluonts, LSTM, Seq2Seq, and ARIMA statistical model, are evaluated using mean absolute percentage error (MAPE) and root mean square error (RMSE) as evaluation parameters. The ARIMA model outperforms cases of acute diarrheal disease with an RMSE value of 317.7 and a MAPE value of 2.4. The Seq2Seq model performs better for dengue cases with an RMSE value of 399.1 and a MAPE value of 6.3. This study provides valuable insights for policymakers in developing and monitoring strategies to combat these diseases. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Programming language and code ARIMA Deep Learning Disease Forecasting LSTM Seq2Seq Model Statistical Model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 11 Nov, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 06 Aug, 2024 Editor assigned by journal 06 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 01 Aug, 2024 First submitted to journal 19 Jul, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4766588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":345692941,"identity":"13af0a35-7e3b-4955-b666-27f8c12328f2","order_by":0,"name":"Avaneesh Singh","email":"","orcid":"","institution":"Indian Institute of Information Technology Design and Manufacturing Jabalpur","correspondingAuthor":false,"prefix":"","firstName":"Avaneesh","middleName":"","lastName":"Singh","suffix":""},{"id":345692942,"identity":"5c97f7ad-9376-4de8-bcef-81a72987c5fa","order_by":1,"name":"Manish Kumar Bajpai","email":"","orcid":"","institution":"Indian 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