Implementation of the Prophet Model in COVID-19 Cases Forecast

Rodiah Rodiah, Eka Patriya, Diana Tri Susetianingtias, Ety Sutanty
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引用次数: 1

Abstract

One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.
先知模型在新冠肺炎病例预测中的应用
了解这一流行病的步骤之一是通过预测各国病例的增加来观察数据的传播,以便尽早进行预防。查看新冠肺炎疫情数据波动的一种方法是使用预测方法预测病例率,以便得出新冠肺炎疫情数据在世界各地传播的结论,并使用统计模型进行处理。这项研究将使用先知模型,在预测过程中使用四个特征,如确诊病例数、康复患者病例数、死亡病例数和活跃病例数,来观察新冠肺炎在世界上的发展速度。这项研究的结果产生了关于新冠肺炎大流行病例数的预测数据,可以每天、每周甚至每月查看。预测结果显示,3月底出现了第一次高峰,直到2020年6月29日,病例数达到1027.58亿例左右,直到2020月29日病例数呈指数级增长。在许多情况下,病例增长率在10月底之前都经历了显著增长,截至2020年10月25日,达到345071.5亿。2020年6月29日之后,一个非常高的峰值与前几个月的病例增加有所不同。预测结果显示,由于新冠肺炎疫情每日确诊病例数的历史数据没有减少,因此没有出现任何下降。本研究的预测结果有望借助有效的周期性数据,系统地预测全球新冠肺炎大流行将发生的事件或事件,从而为新冠肺炎大流行相关的预防措施获取一些信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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