Forecasting and Analyzing Influenza Activity in Hebei Province, China, Using a CNN-LSTM Hybrid Model

Forecasting and Analyzing Influenza Activity in Hebei Province, China, Using a CNN-LSTM Hybrid Model | 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 and Analyzing Influenza Activity in Hebei Province, China, Using a CNN-LSTM Hybrid Model Guofan Li, Yan Li, Guangyue Han, Caixiao Jiang, Minghao Geng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495168/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2024 Read the published version in BMC Public Health → Version 1 posted 16 You are reading this latest preprint version Abstract Background Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network - Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures. Methods Using ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop four distinct forecasting prediction models. We assessed each model’s prediction performance using mean absolute error (MAE) and root mean square error (RMSE). Results The Seasonal Auto-Regressive Indagate Moving Average (SARIMA) model had the highest error among the four forecasting models, with a MAE value of 0.8913 and an RMSE value of 1.2098. The CNN-LSTM model had the lowest error, with MAE and RMSE values of 0.0.3987 and 0.5448, respectively. The CNN-LSTM model thus had a significantly better prediction performance compared to the SARIMA model, with a 55.26% decrease in MAE and a 54.97% decrease in RMSE. When compared to the standalone Convolution Neural Network (CNN) and Long Short Term Memory neural network (LSTM) models, the CNN-LSTM model showed performance enhancements of 32.86% for MAE and 28.60% for RMSE over CNN, and of 11.05% for MAE and 13.07% for RMSE over LSTM. Conclusion The hybrid CNN-LSTM model had better prediction performances than the SARIMA, CNN, and LSTM models. This hybrid model could provide more accurate influenza activity projections in the Hebei Province. Influenza Forecast Deep Learning Hybrid Model Modeling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Aug, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Reviews received at journal 28 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor invited by journal 05 Jun, 2024 Editor assigned by journal 02 Jun, 2024 Submission checks completed at journal 02 Jun, 2024 First submitted to journal 29 May, 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. 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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-4495168","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312853569,"identity":"367ae926-f153-4d05-b81c-f36b6a3cec93","order_by":0,"name":"Guofan Li","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guofan","middleName":"","lastName":"Li","suffix":""},{"id":312853570,"identity":"ff34cd26-0719-4c48-8a01-2b8610e34f83","order_by":1,"name":"Yan Li","email":"","orcid":"","institution":"Hebei Provincial Center for Disease Control and 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Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network - Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop four distinct forecasting prediction models. We assessed each model\u0026rsquo;s prediction performance using mean absolute error (MAE) and root mean square error (RMSE).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Seasonal Auto-Regressive Indagate Moving Average (SARIMA) model had the highest error among the four forecasting models, with a MAE value of 0.8913 and an RMSE value of 1.2098. The CNN-LSTM model had the lowest error, with MAE and RMSE values of 0.0.3987 and 0.5448, respectively. The CNN-LSTM model thus had a significantly better prediction performance compared to the SARIMA model, with a 55.26% decrease in MAE and a 54.97% decrease in RMSE. 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