Spatial modeling of confirmed COVID-19 pandemic in East Java province by geographically weighted negative binomial regression

Abstract

In East Java, 11,910 confirmed incidences for COVID-19 were registered as of 30 June 2020. We propose a Geographically Weighted Negative Binomial Regression (GWNBR) model to evaluate the effect of population density and daily average temperature on COVID-19 transmission. Our results reveal that the areas with high population density have much higher incidences than the areas with a low population density. This result indicates the COVID-19 spread quickly in locations with high population density. So, achieving a reduction in the contact rate between uninfected and infected individuals by quarantined susceptible individuals can effectively reduce disease transmission. However, the average temperature affect spatially only in several areas which shows that there is not enough evidence to explain the effect temperature on COVID-19 cases.