Bivariate Negative Binomial Regression for Modelling Covid-19 Confirmed Cases and Death Cases in Kalimantan

Abstract

Coronavirus disease (Covid-19) caused a pandemic severely affecting various sectors and paralyzed health services in Indonesia. As of June 2020, the percentage of Covid-19 confirmed cases in Kalimantan, the second largest island in Indonesia, contributed about 7% of the total national cases. In the same period, the percentage of Covid-19 deaths reached 12% of the national figure. This study used regression models to respond to bi-response count data consisting of Covid-19 confirmed cases and Covid-19 deaths in regencies/cities in Central Kalimantan and South Kalimantan provinces. This study compared the results of bivariate Poisson regression and bivariate negative binomial regression. There were thirteen predictors representing the determinants of health, social, economic, and demography indicators. The results showed that the prevalence of pneumonia had positive effect on Covid-19 confirmed cases and Covid-19 deaths. The percentage of elderly had negative effect on confirmed cases, while it had no significant effect on Covid-19 deaths. Bivariate negative binomial regression showed more satisfying performance on modeling Covid-19 cases and Covid-19 deaths jointly because it produced lower AIC and deviance than that of Poisson one. The negative bivariate model was also better than the Poisson one because it was able to overcome over-dispersion.