Parvin Rehana
Department of Statistics, International University of Business Agriculture and Technology (IUBAT), Uttara, Dhaka, Bangladesh.
Environ Health Insights. 2023 Jan 18;17:11786302221147455. doi: 10.1177/11786302221147455. eCollection 2023.
Coronavirus-19 (COVID-19) outbreaks have been reported in a range of climates worldwide, including Bangladesh. There is less evidence of a link between the COVID-19 pandemic and climatic variables. This research article's purpose is to examine the relationship between COVID-19 outbreaks and climatic factors in Dhaka, Bangladesh.
The daily time series COVID-19 data used in this study span from May 1, 2020, to April 14, 2021, for the study area, Dhaka, Bangladesh. The Climatic factors included in this study were average temperature, particulate matter ( ), humidity, carbon emissions, and wind speed within the same timeframe and location. The strength and direction of the relationship between meteorological factors and COVID-19 positive cases are examined using the Spearman correlation. This study examines the asymmetric effect of climatic factors on the COVID-19 pandemic in Dhaka, Bangladesh, using the Nonlinear Autoregressive Distributed Lag (NARDL) model.
COVID-19 widespread has a substantial positive association with wind speed ( = .781), temperature ( = .599), and carbon emissions ( = .309), whereas ( = -.178) has a negative relationship at the 1% level of significance. Furthermore, with a 1% change in temperature, the incidence of COVID-19 increased by 1.23% in the short run and 1.53% in the long run, with the remaining variables remaining constant. Similarly, in the short-term, humidity was not significantly related to the COVID-19 pandemic. However, in the long term, it increased 1.13% because of a 1% change in humidity. The changes in PM level and wind speed are significantly associated with COVID-19 new cases after adjusting population density and the human development index.
包括孟加拉国在内,全球多地均报告了新型冠状病毒肺炎(COVID-19)疫情。关于COVID-19大流行与气候变量之间的联系,证据较少。本研究文章旨在探讨孟加拉国达卡市COVID-19疫情与气候因素之间的关系。
本研究使用的每日时间序列COVID-19数据涵盖2020年5月1日至2021年4月14日,研究区域为孟加拉国达卡市。本研究纳入的气候因素包括同一时间框架和地点内的平均温度、颗粒物( )、湿度、碳排放和风速。使用斯皮尔曼相关性检验气象因素与COVID-19阳性病例之间关系的强度和方向。本研究使用非线性自回归分布滞后(NARDL)模型,检验气候因素对孟加拉国达卡市COVID-19大流行的非对称影响效应。
COVID-19传播与风速( = 0.781)、温度( = 0.599)和碳排放( = 0.309)呈显著正相关,而颗粒物( = -0.178)在1%的显著水平上呈负相关。此外,温度每变化1%,短期内COVID-19发病率增加1.23%,长期内增加1.53%,其余变量保持不变。同样,短期内,湿度与COVID-19大流行无显著相关性。然而,从长期来看,湿度每变化1%,发病率增加1.13%。在调整人口密度和人类发展指数后,PM水平和风速的变化与COVID-19新增病例显著相关。