Study on the Spatial Relationships in the Number of Covid-19 Patients based on Ordinary Least Squares Regression and Moran's Spatial Autocorrelation Test Case study (Iran, Lorestan Province) - Ghasem Keikhosravi; Seyedeh Fatemeh Fadavi (Sustainable Earth Trends)
To examine the spatial relationships of the number of patients, effective factors in increasing the number of patients should be determined. The information layers of dependent variables and independent variables were plotted in the GIS system. In the next stage, ordinary least squares regression and the Moran spatial correlation test were used to investigate the significant relationship between the dependent variable and each of the explanatory variables. The results show that the most influential variables in increasing the number of patients are in the first place the urban working population variable and in the second and third place the total population and total working population. The average variable of ambient temperature along with the mentioned variables is an important factor in the release of Covid-19. In the study of the average ambient temperature in three periods, it was found that the number of patients increased with decreasing temperature. Hence, to vaccinate the target groups, it is recommended to vaccinate the urban working population in the first stage, and also the observance of health protocols is strongly recommended in areas where the average ambient temperature is lower.