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Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China

Author

Listed:
  • Rui Zhang

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Zhen Guo

    (Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China)

  • Yujie Meng

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Songwang Wang

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Shaoqiong Li

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Ran Niu

    (National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China)

  • Yu Wang

    (National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China)

  • Qing Guo

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Yonghong Li

    (National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China)

Abstract

Background: This 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. Methods: The 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. Results: The 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. Conclusions: Multivariate 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.

Suggested Citation

  • Rui Zhang & Zhen Guo & Yujie Meng & Songwang Wang & Shaoqiong Li & Ran Niu & Yu Wang & Qing Guo & Yonghong Li, 2021. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China," IJERPH, MDPI, vol. 18(11), pages 1-14, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6174-:d:570438
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    References listed on IDEAS

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    1. Lin Zhu & Zhongshang Yuan & Xianjun Wang & Jie Li & Lu Wang & Yunxia Liu & Fuzhong Xue & Yanxun Liu, 2015. "The Impact of Ambient Temperature on Childhood HFMD Incidence in Inland and Coastal Area: A Two-City Study in Shandong Province, China," IJERPH, MDPI, vol. 12(8), pages 1-14, July.
    2. Lijing Yu & Lingling Zhou & Li Tan & Hongbo Jiang & Ying Wang & Sheng Wei & Shaofa Nie, 2014. "Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhe," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    3. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    4. Ya-wen Wang & Zhong-zhou Shen & Yu Jiang, 2018. "Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-11, September.
    5. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
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    Cited by:

    1. Gonghao Duan & Yangwei Su & Jie Fu, 2023. "Landslide Displacement Prediction Based on Multivariate LSTM Model," IJERPH, MDPI, vol. 20(2), pages 1-16, January.

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