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Abstract：
Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control.
We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks.
From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay.
The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks.
From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay.
The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
摘要：
背景：手，口蹄疫（HFMD）是威胁儿童健康的公共卫生问题。提前几天准确预测手足口病病例，及早发现病例数高峰，并及时作出反应，对于预防和控制手足口病至关重要。然而,许多研究主要预测未来一天的发病率，这降低了预防和控制的灵活性。
方法：收集2011-2017年成都市0-14岁儿童手足口病日发病例数及同期气象、大气污染物数据。LSTM,Seq2Seq,使用Seq2Seq-Luong和Seq2Seq-Shih模型通过多输入多输出对手足口病进行多步预测。我们从整体预测性能的角度评估了模型，检测峰的时间延迟和强度。
结果：从2011年到2017年，成都的手足口病表现出与温度一致的季节性趋势，空气压力,降雨,相对湿度,PM10Seq2Seq-Shih模型实现了最佳性能，RMSE,2天至15天预测的sMAPE和PCC值为13.943~22.192、17.880~27.937和0.887~0.705，分别。同时,Seq2Seq-Shih模型能够以更小的时间延迟检测未来15天内的峰值。
结论：深度学习Seq2Seq-Shih模型在整体和峰值预测方面实现了最佳性能，适用于基于环境因素的手足口病多步预测。
方法：收集2011-2017年成都市0-14岁儿童手足口病日发病例数及同期气象、大气污染物数据。LSTM,Seq2Seq,使用Seq2Seq-Luong和Seq2Seq-Shih模型通过多输入多输出对手足口病进行多步预测。我们从整体预测性能的角度评估了模型，检测峰的时间延迟和强度。
结果：从2011年到2017年，成都的手足口病表现出与温度一致的季节性趋势，空气压力,降雨,相对湿度,PM10Seq2Seq-Shih模型实现了最佳性能，RMSE,2天至15天预测的sMAPE和PCC值为13.943~22.192、17.880~27.937和0.887~0.705，分别。同时,Seq2Seq-Shih模型能够以更小的时间延迟检测未来15天内的峰值。
结论：深度学习Seq2Seq-Shih模型在整体和峰值预测方面实现了最佳性能，适用于基于环境因素的手足口病多步预测。
