Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Int J Environ Res Public Health. 2020 Feb 21;17(4):1381. doi: 10.3390/ijerph17041381.
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.
准确预测流感活动可使卫生官员能够规划疾病预防措施并分配治疗资源。一种很有前途的预测方法是，将成熟的地方性传染病建模框架应用于传染病比例的时间序列。我们使用美国 18 个季节的流感样疾病监测数据，使用适当的评分规则评估了这种新的贝塔自回归模型的概率预测。还使用了其他现成的预测工具进行比较，包括 Prophet、(S)ARIMA 和核条件密度估计 (KCDE)。贝塔模型和 KCDE 都可以在提前四周的时间内对短期流感活动进行同样准确的预测，其中贝塔模型具有四个自回归滞后；然而，贝塔模型的运行速度更快。非动态 Prophet 的得分最差。季节性高峰预测的相对性能有所不同。在具有标准流行曲线的季节中，Prophet 可生成最佳的峰值强度预测；否则，KCDE 优于所有其他方法。季节高峰期的预测最好由 SARIMA、KCDE 或贝塔模型来完成，具体取决于季节。当预测高峰期和强度时，KCDE 的整体性能最佳。只有 KCDE 和简单的历史预测在所有测试季节中始终优于等频参考方法。我们得出结论，地方性传染病贝塔模型是一种性能良好且易于实施的工具，可在几周前预测流感活动。然而，实时预测季节性高峰应同时考虑多个模型的输出，并根据季节的进展衡量它们的有用性。
