Estimating the Upper Limit of Daily Number of COVID-19 Cases in West Java Using Vector Autoregressive Model

2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) Pub Date : 2021-10-13 DOI:10.1109/ICADEIS52521.2021.9701939

Naufal Amiruddin Pratama, A. A. Rohmawati, A. Aditsania

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引用次数: 1

Abstract

The Coronavirus Disease (COVID-19) pandemic of 2019 has spread to over 200 countries and areas throughout the world. The number of confirmed positive COVID-19 cases in Indonesia is increasing every day, notably in West Java, which has the second largest number of confirmed positive COVID-19 cases in Indonesia, with 141,195 instances. The number of daily Covid-19 cases reveal fluctuations, several seasonal and noise patterns. As the significant severity of this pandemic, estimating the future number of the upper limit of daily Covid-19 cases become a major concern to support information and maintain essential public health services. The estimation of the upper limit is carried out in this study utilizing Vector-AR time series process and examined using Value at Risk based historical simulation. Our simulation studies indicate that Vector-AR and historical simulation provide sharp and well estimation for extreme value with a 99% confidence level, infractions on VaR have a minor violation 0.009.

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利用向量自回归模型估计西爪哇省日新冠肺炎病例数上限

2019年新型冠状病毒病(COVID-19)大流行已蔓延至全球200多个国家和地区。印度尼西亚的COVID-19确诊阳性病例每天都在增加，特别是在西爪哇省，该地区的COVID-19确诊阳性病例数量居印度尼西亚第二位，为141195例。每日Covid-19病例数呈现波动、几种季节性和噪音模式。由于这次大流行的严重程度，估计未来每日Covid-19病例上限的数量成为支持信息和维持基本公共卫生服务的主要关注点。本研究利用Vector-AR时间序列过程对上限进行估计，并使用基于风险值的历史模拟进行检验。我们的模拟研究表明，Vector-AR和历史模拟在99%的置信水平上对极值提供了清晰而良好的估计，VaR违规的程度为0.009。

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来源期刊

2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)

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