s 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.

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The outlier is an observation data that has different characteristics from others. Frequently, outliers are removed to improve the accuracy of the estimators. But sometimes the presence of an outlier has a specific meaning, which explanation can be lost if the outlier is removed. There are two exceptional cases from types of outliers, Innovative Outlier (IO) and Additive Outlier (AO). The presence of an outlier in the space-time model is no exception. Space-time model, not only influenced by previous observations at the same location and previous observations in a different location, or there are not only time and location dependencies, but also there are some other things that affect, which can be expressed as an exogenous variable. GSTARX is a model that combines not only time and location but also involves exogenous variables. In the GSTARX model, the presence of outliers may also be detected and may have spatial correlation at a time. In this paper, the iterative procedure in detecting outliers in the GSTARX model was introduced. Therefore data containing outliers is not deleted or ignored but still involves the outlier data by adding an outlier factor to the GSTARX model. The power of the procedure in detecting outliers is investigated by simulation experiments. The result is a GSTARX model with outlier factors that maintain the outlier factor.

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Dengue fever is an endemic disease transmitted through the Aedes Aegypti mosquitos. Dengue virus can be transmitted from human hosts who have been infected by the virus to the mosquitoes to be transmitted back to other humans. So that, it is possible for the virus to be transmitted to several surrounding locations. Aedes Aegypti is one of the dengue mosquitoes that likes a warm climate and not too wet or dry. In addition, many un-expected factors can cause a significant increase in the number of dengue fever cases. So that the number of dengue fever cases can increase significantly far different from other data. An observation data that has different characteristics from others is called outlier. The existence of outliers can indicate individuals or groups that have very different behavior from the most of the individuals of the dataset. Outlier data in a data set are often encountered in various kinds of data analysis. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning, which explanation can be lost if the outlier is removed. In this paper, modeling dengue fever cases using GSTAR(1;1) with outlier factors was firstly proposed.