ARIMA-based time-series analysis for forecasting of COVID-19 cases in Egypt

被引：0
|
作者：
Sabry I. [1 ]
Ismail Mourad A.-H. [2 ,3 ,4 ]
Idrisi A.H. [5 ]
ElWakil M. [6 ]
机构：
[1] Department of Mechanical Engineering, Benha University, Benha
[2] Mechanical and Aerospace Engineering Department, College of Engineering, United Arab Emirates University, P.O. Box. 15551, Al-Ain
[3] National Water and Energy Center, United Arab Emirates University, P.O. Box. 15551, Al Ain
[4] Mechanical Design Department, Faculty of Engineering, Helwan University, El Mataria, Cairo
[5] Department of Mechanical Engineering, UAE University, P.O. Box 15551, Al Ain
[6] Department of Production Engineering and Mechanical Design, Tanta University, Tanta
来源：
International Journal of Simulation and Process Modelling | 2022年 / 19卷 / 1-2期
关键词：
ARIMA; auto-regressive integrated moving average; coronavirus; COVID-19; Egypt; forecast; pandemic;
D O I：
10.1504/IJSPM.2022.130292
中图分类号：
学科分类号：
摘要：
A significant purpose of this study is to examine the distribution of COVID-19 in Egypt to develop an effective forecasting model. It can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of contamination by COVID-19. By this definition, we developed a model and then used it to predict possible COVID-19 cases in Egypt. The analysis suggests a growth trajectory for the events in the days to come. Statistics based on time series analysis and kinetic model analysis indicate that the total case of COVID-19 pneumonia in mainland Egypt can hit 281,478 after a week (March 1, 2020, through July 31, 2021), and the number of simple regenerations can hit 12. Analysis of ARIMA (2, 1, 2) and (2, 1, 3) sequences shows increasing growth in the number of events. © 2022 Inderscience Enterprises Ltd.
引用
收藏
页码：86 / 96
页数：10
相关论文
共 50 条
  • [21] Forecasting Number of COVID-19 Cases in Indonesia with ARIMA and ARIMAX Models
    Aji, Bimo Satrio
    Indwiarti
    Rohmawati, Aniq Atiqi
    2021 9th International Conference on Information and Communication Technology, ICoICT 2021, 2021, : 71 - 75
  • [22] The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis
    He, Zonglin
    Chin, Yiqiao
    Yu, Shinning
    Huang, Jian
    Zhang, Casper J. P.
    Zhu, Ke
    Azarakhsh, Nima
    Sheng, Jie
    He, Yi
    Jayavanth, Pallavi
    Liu, Qian
    Akinwunmi, Babatunde O.
    Ming, Wai-Kit
    JMIR PUBLIC HEALTH AND SURVEILLANCE, 2021, 7 (01): : 231 - 244
  • [23] Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence
    Nguyen, Hieu M.
    Turk, Philip J.
    McWilliams, Andrew D.
    JMIR PUBLIC HEALTH AND SURVEILLANCE, 2021, 7 (08):
  • [24] COVID-19: Forecasting confirmed cases and deaths with a simple time series model
    Petropoulos, Fotios
    Makridakis, Spyros
    Stylianou, Neophytos
    INTERNATIONAL JOURNAL OF FORECASTING, 2022, 38 (02) : 439 - 452
  • [25] Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
    Zeroual, Abdelhafid
    Harrou, Fouzi
    Dairi, Abdelkader
    Sun, Ying
    CHAOS SOLITONS & FRACTALS, 2020, 140
  • [26] Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia
    Al-Turaiki, Isra
    Almutlaq, Fahad
    Alrasheed, Hend
    Alballa, Norah
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (16)
  • [27] Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Model
    Singh, Soni
    Mittal, Sonam
    Singh, Sunaina
    ACCESS 2023 - 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, 2023, : 143 - 148
  • [28] ARIMA-Based Time Series Model of Cutting Temperature in Facing Process
    Dahbi, Samya
    Ezzine, Latifa
    El Moussami, Haj
    JOURNAL OF ADVANCED MANUFACTURING SYSTEMS, 2019, 18 (03) : 395 - 411
  • [29] Identifying diagnosis and mortality of COVID-19 by learning a sequence-to-sequence ARIMA-based model
    Chen, You-Shyang
    Chou, Jerome Chih Lung
    Hsu, Naiying
    Yi Kuo, Ting
    International Journal of Applied Systemic Studies, 2024, 11 (02) : 138 - 158
  • [30] Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA
    Navas Thorakkattle M.
    Farhin S.
    khan A.A.
    Annals of Data Science, 2022, 9 (05) : 1025 - 1047
12345