Modeling the COVID-19 Pandemic Dynamics in Egypt and Saudi Arabia

Mahmoud M. Mansour, Mohammed A. Farsi, Salah M. Mohamed and Enayat M. Abd Elrazik
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Mahmoud M. Mansour: Management Information System Department, Yanbu, Taibah University, Yanbu 46421, Saudi Arabia
Mohammed A. Farsi: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia
Salah M. Mohamed: Department of Applied Statistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
Enayat M. Abd Elrazik: Management Information System Department, Yanbu, Taibah University, Yanbu 46421, Saudi Arabia

Mathematics, 2021, vol. 9, issue 8, 1-13

Abstract: During the abrupt outbreak of the COVID-19 pandemic, the public health system of most of the world’s nations has been tested. However, it is the concern of governments and other responsible entities to provide the correct statistics and figures to take any practicable necessary steps such as allocation of the requisite quarantine operations, calculation of the needed number of places in hospitals, determination of the extent of personal security, and determining the degree of isolation of infectious people, among others. Where the statistical literature supposes that a model governs every real phenomenon, once we know the model, we can evaluate the dilemma. Therefore, in this article, we compare the COVID-19 pandemic dynamics of two neighboring Arabic countries, Egypt and Saudi Arabia, to provide a framework to arrange appropriate quarantine activities. A new generalized family of distributions is developed to provide the best description of COVID-19 daily cases and data on daily deaths in Egypt and Saudi Arabia. Some of the mathematical properties of the proposed family are studied.

Keywords: coronavirus COVID-19 pandemic; Egypt; Saudi Arabia; mathematical modeling; statistical methods; Nadarajah-Haghighi distribution; maximum likelihood; moment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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