• CN 62-1175/P
• ISSN 1006-7639
• 双月刊
• 中国科技核心期刊
• 中国学术期刊综合评价数据库统计源期刊
• 中文科技期刊数据库收录期刊

• 论文 •

### 秦皇岛气象因素对儿童下呼吸道疾病就诊人数影响及预测研究

1. 1.河北省秦皇岛市气象局，河北秦皇岛066000；2.河北省青龙满族自治县气象局，河北青龙066500
• 出版日期:2019-06-28 发布日期:2019-07-01
• 作者简介:李瑞盈（1987— ），女，河北卢龙人，硕士，工程师，主要从事应用气象与气象服务方面的研究. E-mail:rachellry@qq.com。
• 基金资助:
河北省重点研发计划项目（18275402D）和秦皇岛市气象局科研课题（2016003）共同资助

### Study of the Influence of Meteorological Condition on Children Lower Respiratory Tract Infection and the Prediction Model in Qinhuangdao

LI Ruiying1, ZHANG Yibo1, YANG Jia2, ZHAO Ming1, SUN Lihua1, LU Xianmei1

1. 1. Qinhuangdao Meteorological Bureau of Hebei Province, Qinhuangdao 066000, Hebei, China;
2.Qinglong Manchu Autonomous County Meteorological Bureau of Hebei Province, Qinglong 066500, Hebei, China
• Online:2019-06-28 Published:2019-07-01
• About author:孙丽华（1970— ），女，河北唐山人，硕士，高级工程师，主要从事气候与应用气象方面的研究. E-mail: qhdslh@163.com。

Abstract: :Respiratory diseases greatly affected children’s health, and its occurrence was related to meteorological conditions closely. In order to analyze the effects of meteorological conditions on children’s lower respiratory diseases in Qinhuangdao, predict the number of patients and provide new method for medical meteorological service, the data of children with lower respiratory diseases from 2015 to 2016 and the meteorological data within the same time were used, prediction models were established by stepwise regression analysis and BP artificial neutral network separately, and the prediction effects were evaluated. The results show that the meteorological conditions, especially the staged weather changes and climate anomalies, had a significant effect on the patients’ number with these diseases. The number of patients was negatively correlated with air temperature and average relative humidity, and positively correlated with air pressure, wind speed and temperature fluctuation in 72 hours, and good correlation was showed between patient number and air temperature, followed by air pressure and average relative humidity. The prediction accuracy of the stepwise regression model and BP artificial neural network model was 72.75% and 76.30%, respectively. Between the two models established, the overall performance of BP artificial neural network model was better.

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