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Modeling and simulation of the novel coronavirus in Caputo derivative.
Awais, Muhammad; Alshammari, Fehaid Salem; Ullah, Saif; Khan, Muhammad Altaf; Islam, Saeed.
  • Awais M; Department of Mathematics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan.
  • Alshammari FS; Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia.
  • Ullah S; Department of Mathematics University of Peshawar, Pakistan.
  • Khan MA; Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
  • Islam S; Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
Results Phys ; 19: 103588, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-927407
ABSTRACT
The Coronavirus disease or COVID-19 is an infectious disease caused by a newly discovered coronavirus. The COVID-19 pandemic is an inciting panic for human health and economy as there is no vaccine or effective treatment so far. Different mathematical modeling approaches have been suggested to analyze the transmission patterns of this novel infection. this paper, we investigate the dynamics of COVID-19 using the classical Caputo fractional derivative. Initially, we formulate the mathematical model and then explore some the basic and necessary analysis including the stability results of the model for the case when R 0 < 1 . Despite the basic analysis, we consider the real cases of coronavirus in China from January 11, 2020 to April 9, 2020 and estimated the basic reproduction number as R 0 ≈ 4.95 . The present findings show that the reported data is accurately fit the proposed model and consequently, we obtain more realistic and suitable parameters. Finally, the fractional model is solved numerically using a numerical approach and depicts many graphical results for the fractional order of Caputo operator. Furthermore, some key parameters and their impact on the disease dynamics are shown graphically.
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Full text: Available Collection: International databases Database: MEDLINE Topics: Vaccines Language: English Journal: Results Phys Year: 2020 Document Type: Article Affiliation country: J.rinp.2020.103588

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Full text: Available Collection: International databases Database: MEDLINE Topics: Vaccines Language: English Journal: Results Phys Year: 2020 Document Type: Article Affiliation country: J.rinp.2020.103588