ArticleInternational journal of environmental research and public health2021
Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026Article
- Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou.BMC public health · 2025Article
- Stroke Mortality in Kazakhstan: Comparison of National Health Records to Global Burden of Disease Study.JACC. Asia · 2025Article
- Hand, foot and mouth disease incidence in mainland China: temporal trends and projections to 2030.BMC public health · 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
- Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies.MethodsX · 2025Article
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- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.BMC infectious diseases · 2024Article
- Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China.BMC infectious diseases · 2024Article
- Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.BMC infectious diseases · 2024Article
- Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models.Frontiers in public health · 2024Article
- A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM.BMC infectious diseases · 2023Article
- Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.BMC public health · 2023Article
- Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.BMC infectious diseases · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.Epidemiology and infection · 2023Article
- Landslide Displacement Prediction Based on Multivariate LSTM Model.International journal of environmental research and public health · 2023Article
- Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models.PloS one · 2023Article
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9 authors.
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Abstract
backgroundThis study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables.
methodsThe data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models.
resultsThe RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model.
conclusionsMultivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.
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