姜璎慈, 李颖. 气象条件与手足口病发病情况的反向传播神经网络模型构建[J]. 疾病监测, 2018, 33(12): 1026-1031. DOI: 10.3784/j.issn.1003-9961.2018.12.014
引用本文: 姜璎慈, 李颖. 气象条件与手足口病发病情况的反向传播神经网络模型构建[J]. 疾病监测, 2018, 33(12): 1026-1031. DOI: 10.3784/j.issn.1003-9961.2018.12.014
Yingci Jiang, Ying Li. Establishment of back-propagation neural network using meteorological data and incidence data of hand, foot and mouth disease[J]. Disease Surveillance, 2018, 33(12): 1026-1031. DOI: 10.3784/j.issn.1003-9961.2018.12.014
Citation: Yingci Jiang, Ying Li. Establishment of back-propagation neural network using meteorological data and incidence data of hand, foot and mouth disease[J]. Disease Surveillance, 2018, 33(12): 1026-1031. DOI: 10.3784/j.issn.1003-9961.2018.12.014

气象条件与手足口病发病情况的反向传播神经网络模型构建

Establishment of back-propagation neural network using meteorological data and incidence data of hand, foot and mouth disease

  • 摘要:
    目的 运用气象信息和手足口病发病数,构建反向传播神经网络(BPNN)模型,并评价拟合效果,探讨手足口病发病与气象因素的关系。
    方法 收集并按周整理2014 — 2017年上海市长宁区的气象信息和手足口病的发病资料,应用SPSS 13.0统计软件进行Spearman相关分析,用Matlab 7.0软件包中的神经网络工具箱,构建BPNN模型,运用平均误差率(MER)、决定系数( R2)评价拟合效果,运用2017年的手足口病实际发病数检验模型。
    结果 手足口病的周发病数与本周的平均气温、最高气温、最低气温、累计降水量、平均相对湿度、最小相对湿度、平均风速呈正相关(P<0.05),与平均气压呈负相关(r= –0.527,P<0.001)。 与前一周的平均气温、最高气温、最低气温、累计降水量、平均相对湿度、最小相对湿度、平均风速呈正相关(P<0.05),与平均气压呈负相关(r= –0.522,P<0.001)。 利用前一周气象因素拟合的模型效果(MER=19.0%,R2=0.895)略优于利用同周气象因素拟合的模型(MER=20.2%,R2=0.894);2017年气象数据验证模型,预测值和实际值的绝对误差在0 ~ 9之间,平均值为2.52。
    结论 手足口病BPNN预测模型适用于上海市长宁区手足口病发病数的预测。

     

    Abstract:
    Objective In order to analysis the relationship between meteorological factors and the incidence of hand, foot and mouth disease (HFMD), we used weekly meteorological data and the weekly incidence data of HFMD to establish and evaluate the back-propagation neural network (BPNN) model.
    Methods The incidence data of HFMD and meteorological data in Changning district of Shanghai from 2014 to 2017 were collected. Software SPSS 13.0 was used to analyze the relationship between the incidence HFMD and meteorological factors. The prediction model of BPNN was established with software Matlab 7.0. Mean error rate (MER) and determination coefficient (R2) were used to evaluate the fitting effect and the incidence data of HFMD in 2017 was used to verify the model.
    Results The weekly incidence of HFMD was positively correlated with the average temperature, the highest air temperature, the lowest air temperature, cumulative precipitation, relative humidity, minimum relative humidity, average wind speed (all P<0.05), but negatively correlated with the mean air pressure of the same week (r= –0.527, P<0.001). The weekly incidence of HFMD was positively correlated with the average air temperature, the highest air temperature, the lowest air temperature, cumulative precipitation, relative humidity, minimum relative humidity, average wind speed (all P<0.05), but negatively correlated to the mean air pressure of the past week (r= –0.522, P<0.001). The fitting effect using the meteorological data of the past week (MER=19.0%, R2=0.895) was slightly better than that using the data of same week (MER=20.2%, R2=0.894). Verified by meteorological data of 2017, the absolute error between the predicted value and actual value was between 0 and 9 and the average was 2.52.
    Conclusion The BPNN model established is suitable for the prediction of the incidence of HFMD in Changning.

     

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