Spatial Modelling of Covid-19 Confirmed Cases in Kalimantan, Indonesia: How Neighborhood Matters?

The Covid-19 outbreak has hit all countries across the globe, including Indonesia, in which impact is detrimental and costly. We investigated 14 determinants that could spatially influence cases Central Kalimantan South provinces mid-2020 by using Geographically Weighted Negative Binomial Regression (GWNBR) Mixed (MGWNBR). This study conducted iterative Limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundaries (L-BFGS-B) to utilize numerical parameter estimation of MGWNBR. MGWNBR identified adjacent regions tend group 8 clusters containing same significant determinants. Through MGWNBR, comorbid prevalences (acute respiratory infection, pneumonia, diabetes) were positively associated increasing most regions. unemployment rate number health care facilities negatively related increase some was better than GWNBR terms AIC, deviance, pseudo R-sq. residual map also suggested produced a more accurate projection GWNBR. HIGHLIGHTS statistical models for two Indonesia during early stage pandemic Those consisting global, local, mixed estimated effect various social, economical, on model parameters are iteratively L-BFGS-B weighted adaptive bisquare kernel weight Comparison models’ performance applied R-sq GRAPHICAL ABSTRACT