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Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model

Author

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  • Junyi Lu

    (Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany)

  • Sebastian Meyer

    (Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany)

Abstract

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.

Suggested Citation

  • Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1381-:d:323329
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    References listed on IDEAS

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    Cited by:

    1. Zhijuan Song & Xiaocan Jia & Junzhe Bao & Yongli Yang & Huili Zhu & Xuezhong Shi, 2021. "Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020," IJERPH, MDPI, vol. 18(13), pages 1-14, July.
    2. Scher, Vinícius T. & Cribari-Neto, Francisco & Bayer, Fábio M., 2024. "Generalized βARMA model for double bounded time series forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 721-734.
    3. Tian-Shyug Lee & I-Fei Chen & Ting-Jen Chang & Chi-Jie Lu, 2020. "Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme," IJERPH, MDPI, vol. 17(13), pages 1-15, July.
    4. Zixiao Luo & Xiaocan Jia & Junzhe Bao & Zhijuan Song & Huili Zhu & Mengying Liu & Yongli Yang & Xuezhong Shi, 2022. "A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China," IJERPH, MDPI, vol. 19(10), pages 1-12, May.

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