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      Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

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          Abstract

          The aim of this work is to design an intelligent computing paradigm through Levenberg–Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge–Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg–Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.

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          Most cited references 46

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          The species Severe acute respiratory syndrome-related coronavirus : classifying 2019-nCoV and naming it SARS-CoV-2

          The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus–host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.
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            Origin and evolution of pathogenic coronaviruses

            Severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two highly transmissible and pathogenic viruses that emerged in humans at the beginning of the 21st century. Both viruses likely originated in bats, and genetically diverse coronaviruses that are related to SARS-CoV and MERS-CoV were discovered in bats worldwide. In this Review, we summarize the current knowledge on the origin and evolution of these two pathogenic coronaviruses and discuss their receptor usage; we also highlight the diversity and potential of spillover of bat-borne coronaviruses, as evidenced by the recent spillover of swine acute diarrhoea syndrome coronavirus (SADS-CoV) to pigs.
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              COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses

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                Author and article information

                Contributors
                dr.iftikhar@uog.edu.pk
                Journal
                Eur Phys J Plus
                Eur Phys J Plus
                European Physical Journal plus
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2190-5444
                23 November 2020
                2020
                : 135
                : 11
                Affiliations
                [1 ]GRID grid.440562.1, ISNI 0000 0000 9083 3233, Department of Mathematics, , University of Gujrat, ; Gujrat, 50700 Pakistan
                [2 ]GRID grid.412127.3, ISNI 0000 0004 0532 0820, Future Technology Research Center, , National Yunlin University of Science and Technology, ; 123 University Road, Sect. 3, Douliou, Yunlin, 64002 Taiwan R.O.C.
                [3 ]GRID grid.418920.6, ISNI 0000 0004 0607 0704, Department of Electrical and Computer Engineering, , COMSATS University Islamabad, ; Attock Campus, Attock, 43600 Pakistan
                [4 ]GRID grid.418920.6, ISNI 0000 0004 0607 0704, Department of Mathematics, , COMSATS University Islamabad, ; Attock Campus, Attock, 43600 Pakistan
                Article
                910
                10.1140/epjp/s13360-020-00910-x
                7682771
                © Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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                Regular Article
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                © Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2020

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