Burden of COVID-19 Risk in the Municipalities of East Java Province, Indonesia, and its Associated Factors: Spatial Analysis Approach

East Java Province has the fourth-highest number of COVID-19 cases among all other provinces Indonesia. This study aimed to examine the spatial effect on confirmed cases of COVID-19 and the risk factors. Data were analyzed using Geoda software to obtain Global Moran's Index and Local Spatial Autocorrelation (LISA) and QGIS 2.8.1 software to make a map. Moran's I scatter plots also used to exploring the bivariate association between COVID-19 cases and potential predictors. The Global Moran's I statistics value shows spatial clustering in COVID-19 cases across the municipalities of East Java Province (Moran's I=0.3986). A positive spatial autocorrelation was observed between COVID-19 cases and population density (Moran's I = 0.2059), vaccination coverage (Moran's I = 0.322), the number of laboratories (Moran's I = 0.2322), ratio of health worker (Moran's I = 0.1617), and household (Moran's I = 0.0866). In comparison, a negative spatial correlation was observed between COVID-19 cases and The Enforcement of Restrictions on Community Activities' levels (Moran's I =-0,2420), average number of family member (Moran's I = 0.0115). The LISA cluster map shows that there were 3 hot spots (Surabaya, Gresik, and Sidoarjo) and 3 cold spots (Sampang, Pamekasan, and Sumenep). Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

MORAN'S i SPATIAL AUTOCORRELATION of COVID-19 INFECTION RATES: IS THERE SUCH in BUTUAN CITY?

Understanding the spatial autocorrelation of COVID-19 can aid in scientific decision-making in suppressing this infectious disease. This study calculated the cumulative incidence rate of barangays in Butuan City affected by the outbreak of the COVID-19 pandemic from April 15, 2020 to February 28, 2021. This research used Global statistics, i.e., Global Moran's I and Getis-Ord General G, to investigate if spatial autocorrelation occurs in the study region. This study also examines clustering by distance using incremental spatial autocorrelation and multi-distance spatial cluster analysis. The analysis of the hot spot/cold spot and Anselin Local Moran's I were then used to locate high- and low-risk areas of the COVID-19 pandemic. Barangays with a 90-95% confidence interval were identified as significant. Moreover, cluster and outlier analysis demonstrated that ten Barangays exhibit HH-cluster and appear to be high-risk clusters. © ACRS 2021.All right reserved.