GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) FOR COVID-19 CASE IN INDONESIA

Abstract

Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. The response to this ongoing pandemic requires extensive collaboration across the scientific community to contain its impact and limit further transmission. Modeling to see cause-and-effect relationships in an event usually uses the Multiple Linear Regression (Ordinary Least Square) method. But in the case of Covid-19, the spread of the virus occurred from one location to another, so there was an indication that there was a spatial effect on the incident. In this study, we did not only look at spatial perspective but also considered time series data, so the method used was Geographically Weighted Panel Regression (GWPR). This study modeled the number of positive cases of Covid-19 in 34 provinces in Indonesia that occurred from March 2020 to August 2021 and looked at what factors influenced the number of positive cases of Covid-19 in each province. GWPR was performed with the assumption of a Fixed Effect Model (FEM). The FEM assumption was used by considering that the conditions of each observation unit were different. Based on the results, the best GWPR model obtained was the GWPR model with a Fixed Gaussian Kernel. The predictor variables that influenced the number of positive cases of Covid-19 were different at each location and tent to cluster at certain locations.