Abstract
sThe 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.
Highlights
The world has been fighting with the big problem, which has a massive effect on the health sector and almost all sectors in life, that is, pandemic coronavirus disease
On December, 31st 2019, the World Health Organization's (WHO) China office heard the first reports of a previously-unknown virus behind some pneumonia cases in Wuhan, a city in Eastern China (WHO, 2020)
Let follows the Generalized Space Time Autoregressive (GSTAR)(1; 1) model, Zt 1⁄4 Φ10ZtÀ1 þ Φ11WZtÀ1 þ et where Zt is stationary data at time t, Φ10 is the diagonal matrix of autoregressive parameters for first lag of time and zero lag of spatial order, while Φ11 is the diagonal matrix of autoregressive parameters for first lag of time and first lag of spatial order, et is a noise process at time t, and W 1⁄4 1⁄2wij is a matrix, called spatial weight matrix for location j to i
Summary
The world has been fighting with the big problem, which has a massive effect on the health sector and almost all sectors in life, that is, pandemic coronavirus disease. The increasing positive cases are caused by many factors, including (1) high mobility between provinces in Java, (2) DKI Jakarta as the capital city of Indonesia, and (3) many migrants from provinces other than DKI Jakarta on the Java's island who work or live in DKI Jakarta, allowing the movement of people from one province to another province on Java This movement has a significant probability of transmitting the 2019-nCoV virus. According to DKI Jakarta Provincial Government, the train routes within Jabodetabek (Jakarta, Bogor, Depok, Tangerang, and Bekasi) area and from Bogor/Depok to Jakarta City/Angke/Jatinegara, have the highest risk in transmitting the virus, since they serve more than 500,000 people per day Those routes pass three provinces (Banten, DKI Jakarta, and West Java) simultaneously. Conclusions and remarks are put forward in the fourth section
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