| 季风转换对深圳地区呼吸系统疾病的影响及预测研究 |
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| 引用本文： | 吴千鹏,尹立,李兴荣,孙羽,黄开龙,苏春芳,柳艳香,王式功.季风转换对深圳地区呼吸系统疾病的影响及预测研究[J].新疆气象,2023,17(6):32-40. |
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| 作者姓名： | 吴千鹏 尹立 李兴荣 孙羽 黄开龙 苏春芳 柳艳香 王式功 |
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| 作者单位： | 成都信息工程大学大气科学学院/环境气象与健康研究院,攀枝花市中心医院气象医学研究中心,深圳市气象局,海南省第二人民医院气候医学研究中心,广东省汕头市气象局,海南省第二人民医院气候医学研究中心,中国气象局公共气象服务中心,成都信息工程大学大气科学学院/环境气象与健康研究院 |
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| 基金项目： | 攀枝花市科学技术局创新中心建设项目（编号：2021ZX-5-1）、海南省南海气象防灾减灾重点实验室开放基金项目（SCSF202007）和2021年度第二批攀枝花市市级科技计划项目（2021CY-S-4） |
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| 摘 要： | 深圳地处我国华南沿海季风敏感区,为探究季风等气象和污染要素对其呼吸系统疾病发病的影响和其预测相关就诊风险的可行性,本文利用当地2015-2016年呼吸系统疾病就诊人数资料及同期气象和污染物资料,并运用BP人工神经网络和LSTM网络构建呼吸系统疾病就诊人数预测模型。结果显示:每年九月份开始,冬季风的冷胁迫效应会使相关人群呼吸系统疾病发病人数波动式增加,直至次年冬季风向夏季风转换前的三月份发病人数达到峰值；而夏季风控制期间当地居民呼吸系统疾病发病人数呈波动式减少态势,比峰值期间减少35%；另外,该地不同呼吸系统疾病其主控因素也不相同；对比两种预测模型,总体上LSTM网络预报模型对深圳呼吸系统疾病风险预测准确率更高,可以满足健康气象预报服务业务需求。
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| 关 键 词： | 季风转换 呼吸系统疾病 气象与污染条件 人工神经网络 LSTM网络 预测模型 |
| 收稿时间： | 2023/6/4 0:00:00 |
| 修稿时间： | 2023/7/27 0:00:00 |
Study of the influence of monsoon change on respiratory patients and the prediction model in Shenzhen area |
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| Wu Qianpeng,Yin Li,Li Xingrong,Sun Yu,Huang Kailong,Su Chunfang,Liu Yanxiang and Wang Shigong.Study of the influence of monsoon change on respiratory patients and the prediction model in Shenzhen area[J].Bimonthly of Xinjiang Meteorology,2023,17(6):32-40. |
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| Authors: | Wu Qianpeng Yin Li Li Xingrong Sun Yu Huang Kailong Su Chunfang Liu Yanxiang and Wang Shigong |
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| Abstract: | Shenzhen is located in the monsoon sensitive area of South China coast, In order to explore the impact of monsoon and other meteorological conditions and pollution factors on the incidence of respiratory diseases and the feasibility of predicting related medical risks, this paper uses the local data of the number of respiratory disease visits and meteorological and pollutant concentration data in the same period from 2015 to 2016, and uses BP artificial neural network and LSTM network to construct a prediction model for the number of respiratory diseases. The results showed that the cold stress effect of winter wind would increase the incidence of respiratory diseases in related populations fluctuating from September to the peak in March before the transition from winter wind to summer wind in the following year. During the summer wind control period, the incidence of respiratory diseases among local residents fluctuated and decreased, which was 35% lower than that during the peak period. In addition, the main control factors of respiratory diseases in different places are different; Compared with the two prediction models, on the whole, the LSTM network forecasting model has a higher accuracy rate in predicting the risk of respiratory diseases in Shenzhen, which can meet the business needs of health weather forecasting services. |
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| Keywords: | Monsoon transition respiratory diseases meteorological and pollution conditions artificial neural network LSTM network prediction model |
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