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空气污染物和气象因素对COVID-19感染传播和严重性的滞后影响:使用机器学习技术

Nadia Mohammadi Dashtaki¹, Alireza Mirahmadizadeh², Mohammad Fararouei³

¹Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.

Journal of research in health sciences

|September 23, 2024

概括

空气污染和天气显著影响COVID-19的传播和严重程度. 机器学习确定了二氧化,一氧化碳和温度作为影响感染传播和住院的关键因素.

科学领域:

环境科学环境科学
流行病学流行病学
公共卫生公共卫生

背景情况:

空气污染对全球健康构成重大风险.
了解环境因素对传染病的影响至关重要.
这项研究重点关注伊朗什拉兹的COVID-19.

研究的目的:

调查空气污染物和气象参数对COVID-19传播和严重程度的影响.
分析各种污染物 (PM2.5,SO2,NO2,NO,O3,CO) 和天气变量 (T,RH,DP,AP,WS) 及其延迟时间的作用.
应用机器学习技术来预测COVID-19的结果.

主要方法:

采用了生态研究设计.
使用了三种机器学习技术:决策树,随机森林和极端梯度增强 (XGBoost).
数据范围从2020年10月1日到2022年3月1日,将环境因素与COVID-19感染,住院和死亡率相关联.

主要成果:

5天后的二氧化 (NO2),4天后的一氧化碳 (CO) 和25天后的温度 (T) 是对COVID-19传播最有影响的.
住院时间与28天滞后的相对湿度 (RH),11天滞后的温度 (T) 和10天滞后的臭氧 (O3) 有关.
对于COVID-19死亡率,NO2 (滞后20),O3 (滞后22) 和氧化 (NO) (滞后23) 是调整感染后最关键的因素.

结论:

通过空气污染物和气象数据,可以预测COVID-19和类似病毒感染的流行病.
这些环境因素,以及疾病传播动态,有助于预测社区和医疗保健系统的负担.
建议将空气质量和气象数据纳入公共卫生战略,以有效预防.

关键词:

空气污染物空气污染物在 COVID-19 疫情中,机器学习是机器学习.气象学因素气象学因素时间因素时间因素

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