The Generalized STAR Modelling with Minimum Spanning Tree Approach of Weight Matrix for COVID-19 Case in Java Island

Abstract

The ongoing global Coronavirus 2019 (COVID-19) pandemic poses a major threat. The spread of the COVID-19 virus is likely to occur from one location to another location due to the mobility of people. Many efforts and policies have been made by each country to reduce the spread of the COVID-19 outbreak. The imposition of lockdown and large-scale social restrictions or social distancing has been widely applied to limit the transmission of this virus among the community and provincial levels. Both policies have proven effective in reducing the spread of the COVID-19 virus. To obtain the overview of this case, many researchers were conducted. Here, the Generalized STAR (GSTAR) model was applied to model the increasing number of COVID-19 positive cases per day in six provinces in Java Island. The data was recorded simultaneously in six locations, namely in the Provinces of Banten, Jakarta, West Java, Central Java, Yogyakarta Special Region, and East Java. This paper proposes a new approach in constructing the weight matrix required to build the GSTAR model, namely Minimum Spanning Tree (MST). The weight matrix represents the relationship among observed locations. By using the MST, a topological (undirected graph) network model could be created to show the correlation, centrality, and relationship on the increase of COVID-19 positive cases among the provinces in Java Island. The GSTAR(1;1) with the inverse distance weight matrix using MST presents a good ability to predict the COVID-19 increasing cases of Java island. This is indicated by the final MAPE average score of 19.55.