Pasaribu U S, Mukhaiyar U, Huda N M, Sari K N, Indratno S W
Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Indonesia.
Heliyon. 2021 Feb 2;7(2):e06025. doi: 10.1016/j.heliyon.2021.e06025. eCollection 2021 Feb.
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.
2019年发现的新型冠状病毒肺炎（Covid-19，曾称为2019-nCoV）阳性患者从一个地方转移到另一个地方，极有可能将病毒传播给更多人。病毒传播的高风险场所是公共交通工具，其中之一是火车，因为许多人在车内轮流乘坐或一起乘坐。政府的政策之一是保持社交距离，随后实施大规模社会限制。这些政策的关键在于距离和移动。爪哇岛上用于跨省人员流动的最著名交通工具是火车。在此，应用广义时空自回归（GSTAR）模型对爪哇岛6个省份的2019-nCoV感染病例进行预测。该模型的特点是使用权重矩阵作为观察空间依赖性的工具。在此，提出了改进的逆距离权重矩阵，它是人口比例因子与爪哇岛跨省火车平均距离的组合。GSTAR模型（1；1）能够捕捉2019-nCoV每日病例增加的模式，具有代表性的结果证明了这一点，特别是在东爪哇，那里的病例增加受到爪哇岛上其他省份的强烈影响。基于残差平方和，结果表明，与普通矩阵相比，改进后的矩阵在估计（样本内）和预测（样本外）方面都给出了更好的结果。
