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A statistical study of COVID-19 pandemic in Egypt

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Języki publikacji
EN
Abstrakty
EN
The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA)model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.
Wydawca
Rocznik
Strony
233--244
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Department of Mathematics, College of Science and Arts, Qassim University, Ar Rass, Saudi Arabia
  • Department of Mathematics and Statistics, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt
Bibliografia
  • [1] H. A. Rothan and S. N. Byrareddy, The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak, J. Autoimmun. 109 (2020), 102433, DOI: https://doi.org/10.1016/j.jaut.2020.102433.
  • [2] K. Zennir and B. Feng, One spatial variable thermoelastic transmission problem in viscoelasticity located in the second part, Math. Methods Appl. Sci. 41 (2018), no. 16, 6895–6906, DOI: https://doi.org/10.1002/mma.5201.
  • [3] S. Zitouni, A. Ardjouni, K. Zennir, and R. Amiar, Well-posedness and decay of solution for a transmission problem in the presence of infinite history and varying delay, Nonlinear Stud. 25 (2018), 445–465.
  • [4] F. Rohwer, D. Prangishvili, and D. Lindell, Roles of viruses in the environment, Environ Microbiol. 11 (2009), no. 11, 27712774, DOI: https://doi.org/10.1111/j.1462-2920.2009.02101.x.
  • [5] J. W. Tang, The effect of environmental parameters on the survival of airborne infectious agents, J. R. Soc. Interface 6 (2009), no. 6, S737–S746, DOI: https://doi.org/10.1098/rsif.2009.0227.focus.
  • [6] M. A. Al-Qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, Optimization method for forecasting confirmed cases of COVID-19 in China, J. Clin. Med. 9 (2020), no. 3, 674, DOI: https://doi.org/10.3390/jcm9030674.
  • [7] B. Ivorra, M. R. Ferrández, M. Vela-Pérez, and A. Ramos, Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19)taking into account the undetected infections: the case of China, Commun. Nonlinear Sci. Numer. Simul. 88 (2020), 105303, DOI: https://doi.org/10.1016/j.cnsns.2020.105303.
  • [8] R. Tosepu, J. Gunawan, D. S. Effendy, L. O. A. I. Ahmad, H. Lestari, H. Bahar, et al., Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia, Sci. Total Environ. 725 (2020), 138436, https://doi.org/10.1016/j.scitotenv.2020.138436.
  • [9] R. Gupta and S. K. Pal, Trend analysis and forecasting of Covid-19 outbreak in India, MedRxiv, 2020, https://doi.org/10.1101/2020.03.26.20044511.
  • [10] V. Papastefanopoulos, P. Linardatos, and S. Kotsiantis, COVID-19: A comparison of time series methods to forecast percentage of active cases per population, Appl. Sci. 10 (2020), no. 11, 3880, DOI: https://doi.org/10.3390/app10113880.
  • [11] L. Fang, D. Wang, and G. Pan, Analysis and estimation of COVID-19 spreading in Russia based on ARIMA model, SN Compr. Clin. Med. 2 (2020), no. 12, 2521–2527, DOI: https://doi.org/10.1007/s42399-020-00555-y.
  • [12] Z. Malki, E.-S. Atlam, A. Ewis, G. Dagnew, A. R. Alzighaibi, G. ELmarhomy, et al., ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound, Neural. Comput. Appl. 33 (2021), no. 7, 2929–2948, DOI: https://doi.org/10.1007/s00521-020-05434-0.
  • [13] R. Katoch and A. Sidhu, An application of ARIMA model to forecast the dynamics of COVID-19 epidemic in India, Glob. Bus. Rev. (2021), 0972150920988653, DOI: https://doi.org/10.1177/0972150920988653.
  • [14] D. H. Lee, Y. S. Kim, Y. Y. Koh, K. Y. Song, and I. H. Chang, Forecasting COVID-19 confirmed cases using empirical data analysis in Korea, Healthcare 9 (2021), no. 3, 254, DOI: https://doi.org/10.3390/healthcare9030254.
  • [15] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, John Wiley and Sons, 2015.
  • [16] S. I. Alzahrani, I. A. Aljamaan, and E. A. Al-Fakih, Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions, J. Infect. Public Health. 13 (2020), no. 7, 914–919, DOI: https://doi.org/10.1016/j.jiph.2020.06.001.
  • [17] G. Schwarz, Estimating the dimension of a model, Ann. Statist. 6 (1978), no. 2, 461–464, https://doi.org/10.1214/aos/1176344136.
  • [18] H. Akaike, On entropy maximization principle, in: P. R. Krishnaiah (ed.), Applications of Statistics, North-Holland, Amsterdam, 1977, pp. 27–41.
  • [19] E. P. Clement, Using Normalized Bayesian Information Criterion (bic)to improve Box-Jenkins model building, Am. J. Math. Stat. 4 (2014), no. 5, 214–221.
  • [20] https://www.worldometers.info/coronavirus/.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-e5413f1d-ad95-4b16-9d85-2d13667dfd3e
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