Epidemiological characteristics and incidence prediction of hepatitis B in Wuhan, Hubei, 2011−2021
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摘要:
目的 分析湖北省武汉市2011—2021年乙型肝炎(乙肝)的流行病学特征,探讨季节性差分自回归滑动平均(SARIMA)模型与长短期记忆神经网络(LSTM)模型在乙肝发病预测中的应用。 方法 通过描述性流行病学方法分析武汉市2011—2021年乙肝流行特征;建立SARIMA模型和LSTM模型,对武汉市乙肝月报告发病数进行拟合与预测,并评价两种模型的效果。 结果 2011—2021年武汉市报告乙肝累计发病数为55 115例,年均报告发病率为47.37/10万;无明显季节性发病高峰;发病年龄主要集中在30~59岁人群,占比为60.90%;发病人群以家务及待业、不详、农民为主;男、女性别比为2.17∶1,男、女性乙肝年均报告发病率分别为62.64/10万、30.99/10万;中心城区年均报告发病率为44.93/10万,低于远城区的50.96/10万。 SARIMA模型、LSTM模型的均方根误差分别为43.24、16.55,平均绝对百分比误差分别为7.60%、3.05%。 结论 武汉市成年人乙肝防控工作不容忽视,应加强老年人、青年男性等重点人群的乙肝监测、疫苗接种和宣传教育等综合防控工作。 建立的LSTM模型拟合和预测效果总体上优于SARIMA模型。 -
关键词:
- 乙型肝炎 /
- 流行特征 /
- 季节性差分自回归滑动平均模型 /
- 长短期记忆神经网络模型 /
- 预测
Abstract:Objective To analyze the epidemiological characteristics of hepatitis B in Wuhan from 2011 to 2021 and evaluate the application of SARIMA model and LSTM model in predicting the incidence of hepatitis B. Methods The epidemiological characteristics of hepatitis B in Wuhan from 2011 to 2021 were analyzed by descriptive epidemiological method; season autoregressive integrated moving average (SARIMA) model and long short - term memory (LSTM) model were established to fit and predict the monthly reported incidence of hepatitis B in Wuhan. Results A total of 55 115 cases of hepatitis B were reported in Wuhan from 2011 to 2021, the average annual incidence rate was 47.37/100 000. There was no obvious seasonal incidence peak. The cases were mainly people aged 30–59 years old (60.90%). The majority of the cases were the jobless or unemployed, the unspecified and farmers. The men to women ratio of the cases was 2.17∶1, the average annual reported incidence rates in men and women were 62.64/100 000 and 30.99/100 000, respectively. The average annual reported incidence rates in central urban area (44.93/100000) was lower than that in outer urban area (50.96/100000). The root-mean-square-error of the SARIMA model and LSTM model were 43.24 and 16.55, respectively, and the mean absolute percentage error was 7.60% and 3.05%, respectively. Conclusion Close attention still needs to be paid to the prevention and control of hepatitis B in adults in Wuhan, and the comprehensive prevention and control of hepatitis B should be strengthened, including hepatitis B surveillance, vaccination and health education, in the elderly and young men. The LSTM neural network model has better fitting and prediction performance compared with the SARIMA model. -
表 1 2011-2021年武汉市乙型肝炎报告发病情况
Table 1. Reported incidence of hepatitis B in Wuhan, 2011−2021
年份 发病数
(例)发病率
(/10万)急性乙型肝炎 慢性乙型肝炎 未分类乙型肝炎 发病数(例) 发病率(/10万) 发病数(例) 发病率(/10万) 发病数(例) 发病率(/10万) 2011 4 530 46.29 196 2.00 3 977 40.64 357 3.65 2012 4 638 47.13 142 1.44 4 078 41.44 418 4.25 2013 5 400 54.70 138 1.40 4 610 46.70 652 6.60 2014 5 179 51.96 145 1.45 4 861 48.77 173 1.74 2015 5 462 54.63 120 1.20 5 178 51.79 164 1.64 2016 4 924 46.42 117 1.10 4 687 44.18 120 1.13 2017 5 328 49.49 110 1.02 5 109 47.45 109 1.01 2018 4 903 45.01 101 0.93 4 653 42.72 149 1.37 2019 5 411 48.83 105 0.95 5 229 47.19 77 0.69 2020 3 672 32.75 52 0.46 3 501 31.23 119 1.06 2021 5 668 45.98 83 0.67 5 407 43.86 178 1.44 合计 55 115 47.37 1 309 1.13 51 290 44.08 2 516 2.16 表 2 2011-2021年武汉市乙型肝炎报告病例性别分布
Table 2. Gender distribution of reported cases of hepatitis B in Wuhan, 2011−2021
年份 男性 女性 χ2值 P值 发病数
(例)发病率
(/10万)发病数
(例)发病率
(/10万)2011 3 300 65.57 1 230 25.88 831.89 <0.001 2012 3 298 65.04 1 340 28.09 712.21 <0.001 2013 3 761 73.94 1 639 34.25 710.33 <0.001 2014 3 555 69.28 1 624 33.58 611.09 <0.001 2015 3 696 72.21 1 766 36.19 593.45 <0.001 2016 3 328 61.20 1 596 30.87 525.14 <0.001 2017 3 596 65.20 1 732 32.99 564.20 <0.001 2018 3 254 58.51 1 649 30.93 460.19 <0.001 2019 3 630 61.39 1 781 34.46 407.75 <0.001 2020 2 469 41.52 1 203 22.84 297.60 <0.001 2021 3 831 59.84 1 837 31.01 556.38 <0.001 合计 37 718 62.64 17 397 30.99 6 138.29 <0.001 表 3 两种模型的拟合及预测效果评价
Table 3. Evaluation of fitting and prediction effects of two models
精确度评价 时间 SARIMA模型 LSTM模型 RMSE MAPE
(%)RMSE MAPE
(%)拟合效果 43.24 7.60 16.55 3.05 预测效果 2020年1-6月 273.52 283.98 243.83 260.08 2020年7-12月 65.94 14.56 51.05 8.64 2021年1-6月 72.43 16.94 66.98 13.79 2021年7-12月 53.39 9.35 60.05 9.74 注:SARIMA模型. 季节性差分自回归滑动平均模型;LSTM模型. 长短期记忆神经网络模型;RMSE. 均方根误差;MAPE. 平均绝对百分比误差 -
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