ArticleBMC infectious diseases2023
A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM.
Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Age-sex-income triad disparities in diet-attributable diabetes: a GBD 2023 analysis reveals East Asia and Pacific region's decoupling phenomenon and cost-effective solutions.BMJ global health · 2026Article
- Long-Term Trends and Projections of the Global Epilepsy Burden: Insights From the Global Burden of Disease Study 2021.Health care science · 2026Article
- A novel time-series modeling framework for predicting tuberculosis incidence in Sichuan, China.BMC infectious diseases · 2026Article
- Epidemiological characteristics and LSTM-based simulation of mumps incidence in Ningbo, China from 2005 to 2024.BMC public health · 2026Article
- Modeling the seasonal epidemic of human brucellosis in China: A comparative time series analysis.PloS one · 2026Article
- Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models.Frontiers in public health · 2026Article
- Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026Article
- Prediction of the disease burden of nasopharyngeal carcinoma in China: A comparative analysis of ARIMA, LSTM, and DNN models.Medicine · 2025Article
- Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.JMIR infodemiology · 2025Article
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Impact of meteorological factors on the incidence of hand, foot and mouth disease in Ningbo from 2014 to 2019: a causal convolutional neural networks.BMC public health · 2025Article
- Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.BMC public health · 2025Article
- Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq.PloS one · 2025Article
- Analysis of diet-related stroke disease burden in China from 1990 to 2021 and projections for 2025-2044.Frontiers in nutrition · 2025Article
- Epidemiological trends and incidence prediction of periodontal disease based on the Global Burden of Disease study 2021.Frontiers in dental medicine · 2025Article
- A comparative study of time series foundation models for hand, foot, and mouth disease forecasting: TimesFM, Moirai, and traditional approaches.Frontiers in public health · 2025Article
- Application and comparison of ARIMA, LSTM, and ARIMA-LSTM models for predicting foodborne diseases in Liaoning Province.Frontiers in big data · 2025Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
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5 authors.
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Abstract
backgroundHand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD.
methodsThe data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R
resultsOverall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R
conclusionResults showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.
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