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深圳流感发病的气象诱因及预测建模研究

马盼 王馨梓 张莉 唐小新 冉洪雨 王式功

马盼,王馨梓,张莉,唐小新,冉洪雨,王式功. 2022. 深圳流感发病的气象诱因及预测建模研究. 气象学报,80(3):421-432 doi: 10.11676/qxxb2022.039
引用本文: 马盼,王馨梓,张莉,唐小新,冉洪雨,王式功. 2022. 深圳流感发病的气象诱因及预测建模研究. 气象学报,80(3):421-432 doi: 10.11676/qxxb2022.039
Ma Pan, Wang Xinzi, Zhang Li, Tang Xiaoxin, Ran Hongyu, Wang Shigong. 2022. The association between influenza onset and meteorological inducers in Shenzhen and construction of predictive models. Acta Meteorologica Sinica, 80(3):421-432 doi: 10.11676/qxxb2022.039
Citation: Ma Pan, Wang Xinzi, Zhang Li, Tang Xiaoxin, Ran Hongyu, Wang Shigong. 2022. The association between influenza onset and meteorological inducers in Shenzhen and construction of predictive models. Acta Meteorologica Sinica, 80(3):421-432 doi: 10.11676/qxxb2022.039

深圳流感发病的气象诱因及预测建模研究

doi: 10.11676/qxxb2022.039
基金项目: 深圳市气象服务中心“城市安全与民生重点保障服务”项目(20200605)、北京市科技计划课题(Z201100008220002)、甘肃省科技计划项目(21YF5FA169)、商洛气候适应型城市重点实验室基金(SLSYS2019004)、成都信息工程大学大学生创新创业项目(202010621002)
详细信息
    作者简介:

    马盼,主要从事气象环境与健康研究。E-mail: mapan@cuit.edu.cn

    通讯作者:

    唐小新, 主要从事大城市气象防灾应用技术研究。E-mail:786578343@qq.com

  • 中图分类号: P49 X503.1

The association between influenza onset and meteorological inducers in Shenzhen and construction of predictive models

  • 摘要: 深圳市在人口结构和经济社会发展方面很特殊,而对于其热带向副热带过渡的气候特征对流感发病的影响仍缺乏深入研究。本研究收集长序列(2003—2019年)的深圳市流感样病例(Influenza Like Illness,ILI)监测数据,采用分布滞后非线性模型(DLNM)系统分析了ILI与多种气象因子的关联,并分别使用Prophet时间序列和多元逐步回归模型对流感风险进行预报。近17年来深圳ILI发病在2003—2009年增加、2010—2014年平稳、2015—2019年下降,年周期特征凸显;多数年份发病率呈夏季单峰型,与高温、高湿的气候背景高度相符;个别年份在年末出现次高峰,常与大规模暴发疫情有关。DLNM揭示,高温对ILI风险的即时性影响较强,气温达到29.9℃,相对危险度值(RR)可达1.237(95%置信区间(95%CI):1.203—1.272);而低温效应在滞后2—3周起主导作用。70%—75%的湿度范围对应ILI高风险段, 70%相对湿度的RR为1.089(95%CI:1.046—1.135)。偏高的湿度与高温共存可诱使ILI最高风险点出现,即二者有协同增强效应,在其长夏短冬气候下尤其需要注意。ILI危险度在气温日较差为4—6℃或>9℃时均有显著增加,即日内温差对流感的活跃程度亦有显著影响;由于深圳的风速整体较小,其影响整体较弱。Prophet时间序列模型和逐步回归模型的回报准确率相近(>86%),而同时考虑了气象因子和前期发病人数的回归模型预测准确率更高(>80%)。简言之,深圳市ILI风险与温、湿度的非线性协同影响关系最为密切,其发病率很大程度上是可预测的。

     

  • 图 1  2003—2019年深圳市每周ILI就诊人次 (a) 与气象要素 (b—f) 的时间序列(a. 红色虚线为拟合的ILI发病年周期,b. 平均气温,c. 气温日较差,d. 相对湿度,e. 绝对湿度,f. 风速)

    Figure 1.  Time series of weekly ILI cases (a) and meteorological factors (b—f) in Shenzhen from 2003 to 2019 (a. the red dashed line indicates the fitted annual cycles of ILI cases,b. temperature,c. daily temperature range,d. relative humidity,e. absolute humidity,f. wind speed)

    图 2  深圳市2003—2019年流感样病例数及就诊率的逐月分布

    Figure 2.  Monthly distribution of weekly ILI and ILI% in Shenzhen from 2003 to 2019

    图 3  深圳市主要气象要素的频次分布特征 (a. 平均气温,b. 相对湿度,c. 风速,d. 气温日较差)

    Figure 3.  Frequency distributions of main meteorological factors in Shenzhen (a. air temperature,b. relative humidity,c. wind speed,d. daily temperature range)

    图 4  气象要素 (a. 平均气温,b. 相对湿度,c. 风速,d. 气温日较差) 对ILI的累积滞后 (3周) 危险度及其95%置信区间

    Figure 4.  Effects of meteorological variables (a. air temperature,b. relative humidity,c. wind speed,d. daily temperature range) and their cumulative RR(95%CI) on the ILI at lag of 0—3 weeks

    图 5  气温对ILI风险的影响随着滞后时长 (a—d. 滞后0—3周) 的变化

    Figure 5.  Effects of weekly mean temperature on the risk of ILI at different lag periods (a—d. lag 0—3 week)

    图 6  气温分别与 (a) 相对湿度和 (b) 气温日较差对ILI风险交叉效应的三维曲面

    Figure 6.  Three-dimensional curves of cross-effects (a) between temperature and RH,and (b) between temperature and diurnal temperature range

    图 7  风速 (a) 和气温日较差 (b)在滞后0 (a1—b1) 和1 (a2—b2) 周时对ILI相对危险度的影响

    Figure 7.  Effects of wind speed (a) and daily temperature range (b) on ILI RR at lag of 0 (a1—b1) and 1 (a2—b2) week

    图 8  基于 (a) 多元逐步回归和 (b) Prophet时间序列模型的ILI风险预测效果

    Figure 8.  Predicted results of ILI risk based on (a) the stepwise-regression equations and (b) Prophet time-series model

    表  1  深圳市2003—2019年每周流感样病例及气象变量的统计特征

    Table  1.   Statistics of weekly ILI cases and meteorological variables in Shenzhen from 2003 to 2019

    平均值标准差最小值最大值
    ILI925.94390.11342823
    标化ILI (/万人)578.9183.9145.71324.1
    ILI%(%)5.791.841.4613.24
    平均气温 (℃)23.355.188.3930.96
    日最高气温 (℃)26.975.0210.6034.90
    日最低气温 (℃)20.925.306.7928.37
    相对湿度 (%)73.3910.234.5797.0
    绝对湿度/比湿 (g/kg)13.974.943.2822.80
    24 h降水量 (mm)5.008.170.063.50
    气温日较差 (℃)6.051.142.8010.13
    风速 (m/s)2.150.461.193.97
    日照时数 (h)5.172.390.012.03
    平均气压 (hPa)1006.06.23989.61023.6
    下载: 导出CSV

    表  2  深圳的四季划分及其流感样病例特征

    Table  2.   The division of seasons and corresponding ILI characteristics in Shenzhen

    春季夏季秋季冬季
    开始日期2月6日4月21日11月4日1月13日
    季长 (d)761966924
    气温 (℃)18.227.618.214.8
    降水量 (mm)275.41562.566.027.7
    平均ILI(人·次)838.071025.49783.07802.95
    平均ILI%(%)5.646.314.745.01
    下载: 导出CSV

    表  3  周ILI人次与气候因子分季节的斯皮尔曼相关

    Table  3.   Spearman correlations between ILI outpatients and meteorological variables in different seasons

    夏季春秋季冬季全年
    平均气温 (℃)0.219**0.168**−0.0110.341**
    最高气温 (℃)0.175*0.146**0.0630.325**
    最低气温 (℃)0.245**0.191**−0.0590.352**
    气温日较差 (℃)−0.143**−0.165**0.176−0.198**
    相对湿度 (%)0.0370.278**−0.1610.192**
    绝对湿度 (g/kg)0.126**0.262**−0.1000.340**
    降水量 (mm)0.148**0.157**−0.1450.257**
    平均气压 (hPa)−0.102**−0.193**−0.006−0.322**
    风速 (m/s)0.099*−0.217**−0.127−0.070*
    日照时数 (h)0.040−0.165**0.1750.051
     注:**和*分别表示相关系数通过了显著性水平为0.01和0.05 的显著性t检验。
    下载: 导出CSV

    表  4  各气象要素的典型值对ILI的单周滞后相对危险度及其95%置信区间

    Table  4.   RR and 95%CI of individual meteorological variables on ILI risk at various lag times

    滞后0 周滞后1 周滞后2周滞后3周
    日均气温(℃)12.80.854 (0.830—0.878)1.025 (0.997—1.053)1.032 (1.004—1.060)*1.094 (1.067—1.121)*
    29.91.237 (1.203—1.272)*1.058 (1.028—1.088)*1.094 (1.063—1.125)*1.069 (1.040—1.098)*
    相对湿度(%)701.089 (1.046—1.135)*1.044 (1.002—1.087)*1.070 (1.027—1.114)*1.275 (1.225—1.326)*
    910.963 (0.918—1.011)0.981 (0.935—1.029)0.955 (0.911—1.001)1.081 (1.032—1.131)*
    风速(m/s)10.944 (0.916—0.972)1.013 (0.984—1.044)1.058 (1.027—1.089)*0.916 (0.889—0.944)
    31.015 (1.004—1.026)*1.024 (1.013—1.035)*0.991 (0.980—1.002)0.988 (0.977—0.999)
    气温日较差(℃)51.194 (1.141—1.250)*1.051 (1.006—1.099)*1.042 (0.996—1.090)1.049 (1.003—1.096)*
    101.178 (1.110—1.251)*1.055 (0.995—1.118)1.086 (1.025—1.150)*1.018 (0.962—1.078)
     注:*表示相对危险度通过了置信度水平0.05的显著性t检验。
    下载: 导出CSV

    表  5  ILI预报模型优度检验

    Table  5.   Testing indices for the predictive model of ILI risk

    回代拟合试预报
    MAERMSEMAPEP(%)MAERMSEMAPEP(%)
     Prophet模型79.47109.770.13586.49133.59165.970.36368.80
     逐步回归全年66.92 97.560.11788.62 82.21133.220.19980.80
    冷季60.65 90.790.11888.62 88.65162.660.19480.89
    暖季68.11 99.990.11089.16 75.22 87.870.22080.13
    下载: 导出CSV

    表  6  深圳市ILI风险的逐步回归模型

    Table  6.   The stepwise-regression equations of ILI risk in Shenzhen

    回归方程R2
    全年Y=1291.439+0.807ILI'+9.219(ΔT10+3.386(ΔT21−1.171(p110.706
    冷季Y=2473.517+0.794ILI'−6.482(Δp10+4.127(p11+7.995(ΔΔp00.651
    暖季Y=−13.353+0.826ILI'+1.588(RH110.700
     注:(Xa—发病之前a周的气象因子X,即考虑发病前a周的气象条件;(Xba—气象因子 X 在连续b周内进行平均,a同上;ΔX —气象因子X的周平均值相对于上周平均值的差值,即周际变化;ΔΔX—气象因子X的周内标准差,即体现其周内平均变幅。表中各气象因子单位分别为:T (℃),p (hPa),RH (%),V (m/s);ILI'代表前一周病例数。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-17
  • 录用日期:  2022-05-11
  • 修回日期:  2022-03-25
  • 网络出版日期:  2022-04-12

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