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

Abstract

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).