The autoregressive integrated vector model approach for covid-19 data in Indonesia and Singapore

Abstract

Almost all of the countries in the world were attacked by coronavirus disease 2019 (covid-19) which is started from pneumonia endemic cases in Wuhan China in December 2019. The virus spreads including in the Southeast Asian region where Singapore and Indonesia are two countries with the highest cases. Simultaneous modeling of cases in both countries is important as information on the development of covid-19 cases. The Vector Autoregressive Integrated (VARI) model is a multivariate time series model that can be used to build model non-stationary time series data in several locations simultaneously. This study aims to analyze the development of covid-19 cases and builds a VARI model of covid-19 cases in Indonesia and Singapore. The data used from daily covid-19 confirmed cases in the period March 16th until April 19th, 2020. Plotting and statistics testing covid-19 data series from both countries show non-stationary series which have trend and fluctuation. Based on optimum lag identification use the minimum Akaike Information Criteria correction (AICc), the parsimony model is obtained namely VARI(1,1) which has satisfied the multivariate normal, white noise, and homogenous assumption. The result shows that covid-19 cases in Indonesia and Singapore have a strong positive correlation. However, the covid-19 cases in both countries were only influenced by previous cases in each country. The accuracy shows that the model is good enough for forecasting covid-19 cases in both countries.