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Volume 104, Issue 4

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods Open Access

Abstract

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): ARIMA , exponential smoothing , GM (1, 1) , LSTM and monkeypox
Funding
This study was supported by the:
  • Guangxi Medical University Training Program for Distinguished Young Scholars (Award Junjun Jiang)
    • Principle Award Recipient: JunjunJiang
  • Guangxi Bagui Scholar (Award to Junjun Jiang)
    • Principle Award Recipient: JunjunJiang
  • Guangxi Youth Science Fund Project (Award 2021GXNSFBA196004)
    • Principle Award Recipient: WudiWei
  • Guangxi Postdoctoral Special Foundation (Award to Wudi Wei)
    • Principle Award Recipient: WudiWei
  • China Postdoctoral Science Foundation (Award 2020M683212)
    • Principle Award Recipient: WudiWei
  • National Natural Science Foundation of China (Award 31860040)
    • Principle Award Recipient: LiYe
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2023-04-06
2024-05-14
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Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods
J. Gen. Virol. 104, 001839 (2023); https://doi.org/10.1099/jgv.0.001839
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