Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
PLoS One. 2021 Feb 5;16(2):e0246673. doi: 10.1371/journal.pone.0246673. eCollection 2021.
Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.
We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)-neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA-NNAR hybrid model were established for comparison and estimation.
The wavelet-based SARIMA-NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series.
The wavelet-based SARIMA-NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.
手足口病（HFMD）是儿童中最典型的高发病率疾病之一。可靠的预测对于预防和控制至关重要。最近，混合模型变得流行，小波分析得到了广泛应用。基于小波的混合模型可能会获得更好的预测精度。因此，我们的目的是使用基于小波的混合模型对手足口病病例进行预测。
我们拟合了一个基于小波的季节性自回归综合移动平均（SARIMA）-神经网络非线性自回归（NNAR）混合模型，该模型使用了中国郑州 2009 年至 2016 年每周的手足口病病例。此外，还建立了单 SARIMA 模型、单纯 NNAR 模型和纯 SARIMA-NNAR 混合模型进行比较和估计。
与其他模型相比，基于小波的 SARIMA-NNAR 混合模型在拟合和预测方面均表现出优异的性能。其拟合和预测时间序列与实际观察时间序列相似。
本研究中拟合的基于小波的 SARIMA-NNAR 混合模型适用于对手足口病病例数的预测。因此，它将有助于对手足口病的预防和控制。
