This study aims to analyze the spatial correlation of Sar-Cov-19 in northeast region of Brazil considering spatial-temporal characteristics and the association of the number of cases with socio-economic variables. The analysis contemplates all nine states in the region and the number of Sar-Cov-19 cases were obtained from public data of the Brazilian Ministry of Health considering the time points March 06, March 20 and April 07. Socio-economic variables data were collected from the IBGE (Brazilian Institute of Geography and Statistics) and were evaluated according to three approaches: Moran’s Index, Spatial Interpolation and Spatial-Geographic Correlation. Considering the Moran’s Index, there was no clusters with high incidence of the disease on March 06. There were seven clusters on March 20 and 50 on April 07, when Sar-Cov-19 reached the nine state capitals. Regarding to surface analysis, spatial interpolation advanced only from March 20 achieving a huge dissemination on April 07, confirming the highest incidence peaks. The last analysis is the spatial correlation considering as variables: municipality area, estimated population (2019) and population density. March 20 and April 07 data were analyzed, and spatial autocorrelation was verified for residuals. However, a second hypothesis test did not support the hypothesis of residuals autocorrelation with R² values of 0.96 and 0.98 respectively for March 20 and April 07, being a higher performance. Spatial autocorrelation was once more applied for the spatial regression (March 20, Moran's Index = -0.002628 and p = 0.299; April 07, Moran's Index = -0.00268 and p = 0.70), resulting in evidence for not having residuals autocorrelation. Thereby, we can conclude an expressive temporal evolution with a relatively higher number of infected people in capitals and other most populous cities.