The Covid-19 pandemic is considered as a geographical phenomenon, so spatial analysis and its geographical impact on decision-making are very important. Geographic information system and spatial analysis can play an important role in analysis of the spread of Covid-19 at the global level. This study investigated the factors affecting Covid-19 outbreak using global and local spatial regression methods. Altitude, population density, mean age, ratio of population over 55 years to the total population as well as meteorological parameters including humidity, temperature, pressure and wind speed were selected as predictor variables. Population density, air pressure, mean age and wind speed were determined as significant predictors based on the stepwise regression and entered into the OLS. The results showed that the OLS model is statistically significant, but the explanatory variables in the model have an inconsistent relationship to the dependent variable both in geographic space and in data space. Therefore, the GWR was used. To solve the problem of local multicollinearity and increase spatial variability, principal component analysis was used. Finally population density, meteorological and age factors were calculated and used as predictor variables in the GWR. Due to the relative improvement of the performance of this model compared to the general OLS model, it can be concluded that the ability of local models to explain the relationships between these variables is higher than global models. The results of Moran’s I test and hot spot analysis showed that there is at least one variable affecting this disease that has not been considered in this study. However, the results of this study have highlighted the importance of demographic and meteorological factors on the Covid 19 outbreak.