PDF(Pubmed)
Abstract：
BACKGROUND: This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.
METHODS: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.
RESULTS: Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week\'s daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.
CONCLUSIONS: This study\'s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.
METHODS: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.
RESULTS: Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week\'s daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.
CONCLUSIONS: This study\'s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.
摘要：
背景：这项研究采用了完整的气象指标，包括八个项目，探索它们在手上的影响，脚,和口蹄疫（HFMD）在福州，并通过人工智能的长短期记忆(LSTM)神经网络算法预测手足口病的发病率。
方法：采用分布滞后非线性模型(DLNM)分析2010-2021年福州地区气象因素对手足口病的影响。然后，采用LSTM模型,通过多因素单步和多步滚动法预测2019年、2020年和2021年手足口病病例数.均方根误差(RMSE)，平均绝对误差(MAE)，平均绝对百分比误差(MAPE)和对称平均绝对百分比误差(SMAPE)用于评估模型预测的准确性.
结果：总体而言，日降水量对手足口病的影响不显著。低（4hPa）和高（≥21hPa）每日气压差（PRSD）以及低（<7°C）和高（>12°C）每日空气温差（TEMD）是HFMD的危险因素。RMSE,MAE,使用每周多因素数据预测次日手足口病病例的MAPE和SMAPE，从2019年到2021年，低于使用每日多因素数据预测次日手足口病病例的数据。特别是,RMSE,MAE,使用每周多因素数据预测下一周手足口病日平均病例的MAPE和SMAPE要低得多，在城市和农村地区也发现了类似的结果，这表明这种方法更准确。
结论：本研究的LSTM模型结合气象因素（不包括PRE）可用于准确预测福州市手足口病，特别是使用每周多因素数据预测下一周手足口病日平均病例的方法。
方法：采用分布滞后非线性模型(DLNM)分析2010-2021年福州地区气象因素对手足口病的影响。然后，采用LSTM模型,通过多因素单步和多步滚动法预测2019年、2020年和2021年手足口病病例数.均方根误差(RMSE)，平均绝对误差(MAE)，平均绝对百分比误差(MAPE)和对称平均绝对百分比误差(SMAPE)用于评估模型预测的准确性.
结果：总体而言，日降水量对手足口病的影响不显著。低（4hPa）和高（≥21hPa）每日气压差（PRSD）以及低（<7°C）和高（>12°C）每日空气温差（TEMD）是HFMD的危险因素。RMSE,MAE,使用每周多因素数据预测次日手足口病病例的MAPE和SMAPE，从2019年到2021年，低于使用每日多因素数据预测次日手足口病病例的数据。特别是,RMSE,MAE,使用每周多因素数据预测下一周手足口病日平均病例的MAPE和SMAPE要低得多，在城市和农村地区也发现了类似的结果，这表明这种方法更准确。
结论：本研究的LSTM模型结合气象因素（不包括PRE）可用于准确预测福州市手足口病，特别是使用每周多因素数据预测下一周手足口病日平均病例的方法。
