北京市主要天气敏感性疾病发病与流行的24节气特征分析和预报模型构建

发布时间：2018-04-13 01:17

本文选题：上呼吸道感染 + 支气管炎　；参考：《兰州大学学报(自然科学版)》2017年03期

【摘要】：利用北京市2009-2011年疾病数据和同期气象资料,从24节气的角度分析了北京市近年来上呼吸道感染、支气管炎和脑梗死的发病与流行时间变化特征,发现呼吸系统疾病的发病受干冷空气影响较大,体现了以冷效应为主的特征,春季此类疾病的发病人数明显减少;循环系统疾病发病峰值期的出现,主要是秋末冬初冷暖空气频繁交替所致,与气温的变化幅度与频次密切相关.两种疾病发病的气象成因有一定差异,建立了北京市相关天气敏感性疾病发病的逐月预报方程,分别进行了回代检验和试预报检验,结果表明,回代检验中3种疾病的逐月预报方程均较好的反映了当天的患病人数;试预报结果不如回代检验的结果,且呈现夏季暖湿天气条件下呼吸系统疾病发病人数最少,干冷的冬季及粉尘较多的春季为呼吸系统疾病流行高发期的季节变化特征.预报方程在描述呼吸系统疾病发病高峰期时在数值上存在一定偏差,说明发病高峰期并不仅仅与气象因子有关,可能还受环境、空气污染及社会因素等影响.构建的逐月预报方程充分考虑了疾病发病的滞后效应和周末效应,利用了扩展后的368个气象因子进行优化筛选,充分体现了主控因子的主导作用,能够对相关天气敏感性疾病发病情况做出较好预报.
[Abstract]:The use of 2009-2011 years in Beijing City, disease data and meteorological data from the point of view of the 24 solar term in Beijing city in recent years the incidence and prevalence of upper respiratory tract infection, bronchitis and time variation of cerebral infarction, found that respiratory disease affected by the dry cold air influence, reflects the characteristics of the cold effect, significantly reduced the incidence of spring this kind of disease; circulation system diseases peak period, is mainly caused by frequent alternation of autumn and winter cold and warm air, is closely related with the changes of the amplitude and frequency of temperature. The meteorological causes two kinds of diseases have certain differences, set up monthly forecast equation of Beijing city related weather sensitive disease, were carried out to test test and prediction test, the results show that the monthly forecast equations of 3 kinds of diseases in the back substitution test can better reflect the prevalence of the pre trial; The reported results as to the result of the test, and in summer the warm weather conditions of respiratory diseases in winter and the lowest number, more dust cold spring for the seasonal variation of respiratory disease in the period of high popularity. The prediction equation in describing the respiratory system disease peak in value there is a certain deviation, indicating the peak incidence not only related with meteorological factors, and may also be affected by the environment, the effect of air pollution and social factors. The monthly forecast equation constructed considering the lag effect of disease and the weekend effect, with the expansion of the 368 meteorological factors were optimized, fully reflects the leading role of the main control factors, can make better prediction of weather related sensitive disease incidence.

【作者单位】：兰州大学大气科学学院甘肃省干旱气候变化与减灾重点实验室;成都信息工程大学大气科学学院;
【基金】：国家自然科学基金项目(91644226,41575138) 国家基础科技条件平台建设项目(NCMI-SBS17-201607,2016NCMIZX09,NCMI-SJS15-201607) 国家公益性行业(气象)科研专项项目(GYHY201306047,GYHY201106034)
【分类号】：R122.26

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