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许晓萌, 崔世恒, 王亚菲, 孙丽, 丛艳丽, 王晶辉, 李静, 张振国. 长短时记忆神经网络模型在河北省麻疹疫情发病趋势预测中应用[J]. 中国公共卫生, 2023, 39(11): 1464-1468. DOI: 10.11847/zgggws1141808
引用本文: 许晓萌, 崔世恒, 王亚菲, 孙丽, 丛艳丽, 王晶辉, 李静, 张振国. 长短时记忆神经网络模型在河北省麻疹疫情发病趋势预测中应用[J]. 中国公共卫生, 2023, 39(11): 1464-1468. DOI: 10.11847/zgggws1141808
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XU Xiaomeng, CUI Shiheng, WANG Yafei, SUN Li, CONG Yanli, WANG Jinghui, LI Jing, ZHANG Zhenguo. Predicting trend of measles epidemic in Hebei province: an empirical study with long short-term memory neural network model[J]. Chinese Journal of Public Health, 2023, 39(11): 1464-1468. DOI: 10.11847/zgggws1141808
Citation: XU Xiaomeng, CUI Shiheng, WANG Yafei, SUN Li, CONG Yanli, WANG Jinghui, LI Jing, ZHANG Zhenguo. Predicting trend of measles epidemic in Hebei province: an empirical study with long short-term memory neural network model[J]. Chinese Journal of Public Health, 2023, 39(11): 1464-1468. DOI: 10.11847/zgggws1141808
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长短时记忆神经网络模型在河北省麻疹疫情发病趋势预测中应用

Predicting trend of measles epidemic in Hebei province: an empirical study with long short-term memory neural network model

    摘要
  • 摘要:
      目的  探讨长短时记忆（LSTM）神经网络模型在麻疹疫情发病趋势预测上的可行性，为科学防控麻疹提供参考依据。
      方法  收集中国疾病预防控制信息系统传染病监测系统中河北省发病日期为2004年1月 — 2020年12月的51012例麻疹病例发病数据构建LSTM神经网络模型，选择最优模型对河北省麻疹疫情发病趋势进行预测，并采用均方误差平方根（RMSE）和平均绝对误差（MAE）评价模型预测效果。
      结果  河北省2004、2005、2006、2007、2008、2009、2010、2011、2012、2013、2014、2015、2016、2017、2018、2019和2020年分别报告麻疹病例950、4837、7953、4973、2273、3359、14457、79、38、353、5365、3825、1825、287、241、130和67例，从2015年开始河北省麻疹发病数逐年下降，且发病具有明显的季节性；视窗长度分析结果显示，当视窗长度取3时，模型预测效果最好，RMSE和MAE值分别为17.288和12.334；本研究构建LSTM神经网络模型对河北省2017 — 2020年麻疹发病情况进行预测，模型预测的发病趋势与实际趋势基本一致，RMSE和MAE值在2017、2019和2020年均 < 10，但2018年误差略大。
      结论  LSTM神经网络模型在河北省麻疹疫情发病趋势预测中效果较好，可用于麻疹发病趋势的研判和风险评估。

     

    Abstract:
      Objective  To explore the feasibility of predicting the trend of measles epidemic using long short-term memory (LSTM) neural network model for conducting prevention and control of measles.
      Methods  The data on 51 012 measles cases reported in Hebei province form 2004 through 2020 were collected from China Information System for Disease Control and Prevention. The LSTM neural network model was constructed and the optimal model was selected to predict the trend of measles epidemic in the province. Rooted mean squared error (RMSE) and mean absolute error (MAE) were used to evaluate the prediction of model established.
      Results  The annual number of measles cases reported in the province during the 17-year period were 950, 4 837, 7 953, 4 973, 2 273, 3 359, 14 457, 79, 38, 353, 5 365, 3 825, 1 825, 287, 241, 130, and 67, respectively, with a persistent decline since 2015. In addition, an obvious seasonality was observed in the incidence of measles. Using the collected data of 2017, the window length of 3 was determined for the constructed LSTM neural network model, with the RMSE of 17.288 and the MAE of 12.334, and the model was adopted to predict monthly number of measles cases from 2017 through 2020. The predicted monthly numbers of measles incidence were basically consistent with the number observed and the values of RMSE and MAE for years of 2017, 2019 and 2020 were all below 10, but the values for 2018 were slightly higher.
      Conclusion  The constructed LSTM neural network model in this study showed a good efficiency in predicting monthly measles incidence in Hebei province and the model could be used in the analysis on measles incidence trend and epidemic risk assessment.

     

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