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Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States

Author

Listed:
  • Bowen Long

    (Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA)

  • Fangya Tan

    (Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA)

  • Mark Newman

    (Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA)

Abstract

Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R 2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy.

Suggested Citation

  • Bowen Long & Fangya Tan & Mark Newman, 2023. "Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States," Forecasting, MDPI, vol. 5(1), pages 1-11, January.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:5-137:d:1027134
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    References listed on IDEAS

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    1. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. 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.
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