COVID-19 Epidemic Process Simulation Using ARIMA Model

被引：1

作者：

Mohammadi, Alireza ^{[1
]}

Chumachenko, Dmytro ^{[1
]}

机构：

[1] Natl Aerosp Univ, Kharkiv Aviat Inst, Kharkiv, Ukraine

来源：

INTEGRATED COMPUTER TECHNOLOGIES IN MECHANICAL ENGINEERING - 2021 | 2022年 / 367卷

基金：

新加坡国家研究基金会;

关键词：

Epidemic model; Machine learning; Epidemic process simulation; COVID-19; forecasting;

D O I：

10.1007/978-3-030-94259-5_31

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The substantial ascendant trend within the number of daily infected new cases with coronavirus around the world is a warning, and several other researchers are utilizing various mathematical and machine learning-based prediction models to forecast the long-term trend of the COVID-19 pandemic. During this research, the Autoregressive Integrated Moving Average or ARIMA model was implemented to forecast the COVID-19 expected daily number of cases in Ukraine. We implemented Autoregressive Integrated Moving Average for this research. The forecasting results showed that the trend in Ukraine will continue ascending and should reach up to more than 1.8 million total cases if stringent precautionary and control measures don't get implemented to prohibit the spread of COVID-19.

引用

页码：353 / 363

页数：11

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