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Mathematical modeling and analysis of COVID-19: A study of new variant Omicron.
Khan, Muhammad Altaf; Atangana, Abdon.
  • Khan MA; Faculty of Natural and Agricultural Sciences, University of the Free State, South Africa.
  • Atangana A; Faculty of Natural and Agricultural Sciences, University of the Free State, South Africa.
Physica A ; 599: 127452, 2022 Aug 01.
Article in English | MEDLINE | ID: covidwho-1804983
ABSTRACT
We construct a new mathematical model to better understand the novel coronavirus (omicron variant). We briefly present the modeling of COVID-19 with the omicron variant and present their mathematical results. We study that the Omicron model is locally asymptotically stable if the basic reproduction number R 0 < 1 , while for R 0 ≤ 1 , the model at the disease-free equilibrium is globally asymptotically stable. We extend the model to the second-order differential equations to study the possible occurrence of the layers(waves). We then extend the model to a fractional stochastic version and studied its numerical results. The real data for the period ranging from November 1, 2021, to January 23, 2022, from South Africa are considered to obtain the realistic values of the model parameters. The basic reproduction number for the suggested data is found to be approximate R 0 ≈ 2 . 1107 which is very close to the actual basic reproduction in South Africa. We perform the global sensitivity analysis using the PRCC method to investigate the most influential parameters that increase or decrease R 0 . We use the new numerical scheme recently reported for the solution of piecewise fractional differential equations to present the numerical simulation of the model. Some graphical results for the model with sensitive parameters are given which indicate that the infection in the population can be minimized by following the recommendations of the world health organizations (WHO), such as social distances, using facemasks, washing hands, avoiding gathering, etc.
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Full text: Available Collection: International databases Database: MEDLINE Topics: Variants Language: English Journal: Physica A Year: 2022 Document Type: Article Affiliation country: J.physa.2022.127452

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Full text: Available Collection: International databases Database: MEDLINE Topics: Variants Language: English Journal: Physica A Year: 2022 Document Type: Article Affiliation country: J.physa.2022.127452