The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

IF 1.4 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Journal of research in health sciences Pub Date : 2024-08-01 Epub Date: 2024-07-31 DOI:10.34172/jrhs.2024.157
Nadia Mohammadi Dashtaki, Alireza Mirahmadizadeh, Mohammad Fararouei, Reza Mohammadi Dashtaki, Mohammad Hoseini, Mohammad Reza Nayeb
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引用次数: 0

Abstract

Background: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran. Study Design: An ecological study.

Methods: In this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO2 ), nitrogen dioxide (NO2 ), nitric oxide (NO), ozone (O3 ), carbon monoxide (CO), temperature (T), relative humidity (RH), dew point (DP), air pressure (AP), and wind speed (WS).

Results: Based on the three ML techniques, NO2 (lag 5 day), CO (lag 4), and T (lag 25) were the most important environmental features affecting the spread of COVID-19 infection. In addition, the most important features contributing to hospitalization due to COVID-19 included RH (lag 28), T (lag 11), and O3 (lag 10). After adjusting for the number of infections, the most important features affecting the number of deaths caused by COVID-19 were NO2 (lag 20), O3 (lag 22), and NO (lag 23).

Conclusion: Our findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures.

空气污染物和气象因素对 COVID-19 感染传播和严重程度的滞后效应:使用机器学习技术。
背景:暴露于空气污染是全世界的一个主要健康问题。本研究旨在利用机器学习(ML)技术研究伊朗设拉子的空气污染物水平和气象参数及其相关滞后时间对冠状病毒疾病 19(COVID-19)的传播和严重程度的影响。研究设计:生态研究:在这项生态研究中,应用了三种主要的 ML 技术,包括决策树、随机森林和极端梯度提升(XGBoost),将 2020 年 10 月 1 日至 2022 年 3 月 1 日期间的气象参数和空气污染物与 COVID-19 导致的感染传播、住院和死亡相关联。这些参数和污染物包括颗粒物(PM2)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化氮(NO)、臭氧(O3)、一氧化碳(CO)、温度(T)、相对湿度(RH)、露点(DP)、气压(AP)和风速(WS):结果:根据三种 ML 技术,二氧化氮(滞后 5 天)、一氧化碳(滞后 4 天)和温度(滞后 25 天)是影响 COVID-19 感染传播的最重要环境特征。此外,导致 COVID-19 住院的最重要特征包括相对湿度(滞后 28 天)、T(滞后 11 天)和 O3(滞后 10 天)。在对感染人数进行调整后,影响 COVID-19 导致的死亡人数的最重要特征是 NO2(滞后 20)、O3(滞后 22)和 NO(滞后 23):我们的研究结果表明,COVID-19 和(可能)类似的病毒传播感染(包括流感)引起的流行病、空气污染物和气象参数可用于预测其对社区和卫生系统造成的负担。此外,气象和空气质量数据也应纳入预防措施中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of research in health sciences
Journal of research in health sciences PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
2.30
自引率
13.30%
发文量
7
期刊介绍: The Journal of Research in Health Sciences (JRHS) is the official journal of the School of Public Health; Hamadan University of Medical Sciences, which is published quarterly. Since 2017, JRHS is published electronically. JRHS is a peer-reviewed, scientific publication which is produced quarterly and is a multidisciplinary journal in the field of public health, publishing contributions from Epidemiology, Biostatistics, Public Health, Occupational Health, Environmental Health, Health Education, and Preventive and Social Medicine. We do not publish clinical trials, nursing studies, animal studies, qualitative studies, nutritional studies, health insurance, and hospital management. In addition, we do not publish the results of laboratory and chemical studies in the field of ergonomics, occupational health, and environmental health
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