Like all infectious diseases, the infection rate of COVID-19 is dependent on many variables. In order to effectively prepare a localized plan for infectious disease management, it is important to find the relationship between COVID-19 infection rate and other key variables. This study aims to understand the spatial relationships between COVID-19 infection rate and key variables of air pollution, geo-meteorological, and social parameters in Dhaka, Bangladesh. The relationship was analyzed using Geographically Weighted Regression (GWR) model and Geographic Information System (GIS) by means of COVID-19 infection rate as a dependent variable and 17 independent variables. This study revealed that air pollution parameters like PM_2.5 (p < 0.02), AOT (p < 0.01), CO (p < 0.05), water vapor (p < 0.01), and O₃ (p < 0.01) were highly correlated with COVID-19 infection rate while geo-meteorological parameters like DEM (p < 0.01), wind pressure (p < 0.01), LST (p < 0.04), rainfall (p < 0.01), and wind speed (p < 0.03) were also similarly associated. Social parameters like population density (p < 0.01), brickfield density (p < 0.02), and poverty level (p < 0.01) showed high coefficients as the key independent variables to COVID-19 infection rate. Significant robust relationships between these factors were found in the middle and southern parts of the city where the reported COVID-19 infection case was also higher. Relevant agencies can utilize these findings to formulate new and smart strategies for reducing infectious diseases like COVID-19 in Dhaka and in similar urban cities around the world. Future studies will have more variables including ecological, meteorological, and economical to model and understand the spread of COVID-19.

与所有传染病一样，COVID-19 的感染率取决于许多变量。为了有效地制定本地化传染病管理计划，找到 COVID-19 感染率与其他关键变量之间的关系非常重要。本研究旨在了解孟加拉国达卡的 COVID-19 感染率与空气污染、地理气象和社会参数等关键变量之间的空间关系。使用地理加权回归 (GWR) 模型和地理信息系统 (GIS) 分析这种关系，以 COVID-19 感染率作为因变量和 17 个自变量。这项研究表明，PM _2.5 ( p  < 0.02)、AOT ( p  < 0.01)、CO ( p  < 0.05)、水蒸气 ( p  < 0.01) 和 O ₃ ( p  < 0.01) 等空气污染参数与COVID-19 感染率，同时 DEM ( p  < 0.01)、风压 ( p  < 0.01)、LST ( p  < 0.04)、降雨量 ( p  < 0.01) 和风速 ( p  < 0.03) 等地理气象参数也受到影响类似地关联。人口密度 ( p  < 0.01)、砖地密度 ( p  < 0.02) 和贫困水平 ( p  < 0.01) 等社会参数显示出作为 COVID-19 感染率的关键自变量的高系数。在该市中部和南部地区发现这些因素之间存在显着的密切关系，这些地区报告的 COVID-19 感染病例也较高。相关机构可以利用这些发现制定新的明智战略，以减少达卡和世界各地类似城市的 COVID-19 等传染病。未来的研究将有更多变量（包括生态、气象和经济）来建模和了解 COVID-19 的传播。