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

Abstract

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