Spatial analysis and risk factors associated with COVID-19 incidence modeling in Sleman Regency

Abstract

Purpose: This research aims to identify spatial distribution and risk factors related to the occurrence of COVID-19 in Sleman Regency.

Methods: This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases. Pearson correlation and linear regression examined the relationship between the selected variables and COVID-19 incidence. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I and LISA. Geographically weighted regression (GWR) and multiscale GWR (MGWR) were used to examine the local level.

Results: Multivariate analysis results showed that shopping facilities (coeff. =10.02; p-value <0.001) and population density (coeff. =0.0004; p-value <0.001). The spatial autocorrelation test showed a positive and significant spatial autocorrelation between the presence of public facilities (Moran's I=0.600) and population density (Moran's I=0.495) with the incidence of COVID-19 in Sleman Regency. The MGWR model has been proven to be the most appropriate in describing the incidence of COVID-19 in the Sleman Regency (adj R 2 =0.643; AIC c =177.14).

Conclusion: The spatial approach has been used to prevent the spread of COVID-19. For example, micro-based social restriction monitoring efforts and COVID-19 vaccination campaigns can focus more on areas with more shopping facilities and densely populated areas.