Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Model

Soni Singh, S. Mittal, Sunaina Singh
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Abstract

The global community is now seriously threatened by the COVID-19 pandemic. The government of every nation must pay close attention to the analysis of this disease to take the required actions to lessen the impact of this worldwide epidemic. This research focused on the disease outbreak in the Indian region through July 21st, 2021, and evaluated the incidence and mortality. Machine learning techniques, such as the ARIMA model, are applied to perform the prediction analysis on collected data from the World Health Organization (WHO) official portal for India between January 20, 2020, and July 21, 2021. Mean Square Error (MSE), a measure of model performance, was used to assess performance, and it came in between 2170.636098 and 46.839689. In the four weeks of test data, the Expected instances are estimated to be between 192K and 230K, which is fairly similar to the actual figures. The government and physicians will be able to make future strategies with the aid of this study.
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基于ARIMA模型的COVID-19大流行分析与预测
当前,国际社会正受到COVID-19大流行的严重威胁。各国政府必须密切关注对这一疾病的分析,采取必要的行动,减轻这一世界性流行病的影响。本研究以截至2021年7月21日的印度地区疫情为研究对象,评估了发病率和死亡率。机器学习技术,如ARIMA模型,用于对2020年1月20日至2021年7月21日期间从世界卫生组织(世卫组织)印度官方门户网站收集的数据进行预测分析。均方误差(MSE)是衡量模型性能的一种方法,用于评估性能,其范围在2170.636098和46.839689之间。在四周的测试数据中,预期实例估计在192K到230K之间,这与实际数字相当相似。政府和医生将能够在这项研究的帮助下制定未来的策略。
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