Nafiz Rahaman Sk, Shehzad Tanvir, Sultana Maria
Urban and Rural Planning Discipline, Khulna University, Khulna, Bangladesh.
Environ Health Insights. 2022 Oct 16;16:11786302221131467. doi: 10.1177/11786302221131467. eCollection 2022.
This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.
本研究旨在确定季节性地表温度变化对新冠肺炎感染率的影响。本研究的区域为孟加拉国及其8个行政区。利用谷歌地球引擎（GEE）平台从2020年5月至2021年7月的中分辨率成像光谱仪（MODIS）卫星图像中提取地表温度（LST）值。还收集了同一日期范围内每天的新增新冠肺炎病例数据。描述性和统计结果表明，在经历了高地表温度季节后，新增新冠肺炎病例数上升。另一方面，当地表温度在冬季下降时，新冠肺炎感染率降低。此外，地表温度的快速起伏导致大量新增病例。流动性、社交互动和意外的天气变化可能是地表温度与新冠肺炎感染率之间这种关系背后的主要因素。
