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The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

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  • Yu-Tse Tsan

    (Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 407204, Taiwan
    School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
    Division of Occupational Medicine, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 407204, Taiwan)

  • Der-Yuan Chen

    (College of Medicine, China Medical University, Taichung 406040, Taiwan
    Rheumatology and Immunology Center, China Medical University Hospital, Taichung 404332, Taiwan)

  • Po-Yu Liu

    (Division of Infection, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40704, Taiwan)

  • Endah Kristiani

    (Department of Computer Science, Tunghai University, Taichung 407224, Taiwan
    Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia)

  • Kieu Lan Phuong Nguyen

    (Faculty of Environmental and Food Engineering, Nguyen Tat Thanh University, Ho Chi Minh City 70000, Vietnam)

  • Chao-Tung Yang

    (Department of Computer Science, Tunghai University, Taichung 407224, Taiwan
    Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan)

Abstract

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

Suggested Citation

  • Yu-Tse Tsan & Der-Yuan Chen & Po-Yu Liu & Endah Kristiani & Kieu Lan Phuong Nguyen & Chao-Tung Yang, 2022. "The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA," IJERPH, MDPI, vol. 19(3), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1858-:d:743644
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    References listed on IDEAS

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    1. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    2. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
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    Cited by:

    1. Yu-Tse Tsan & Endah Kristiani & Po-Yu Liu & Wei-Min Chu & Chao-Tung Yang, 2022. "In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning," IJERPH, MDPI, vol. 19(11), pages 1-19, May.

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