Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility.

ABSTRACT

The movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.