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Coronaviruses

Editor-in-Chief

ISSN (Print): 2666-7967
ISSN (Online): 2666-7975

Review Article

A Coadunation of Biological and Mathematical Perspectives on the Pandemic COVID-19: A Review

Author(s): Sahar Qazi, Kayenat Sheikh, Mo Faheem, Arshad Khan and Khalid Raza*

Volume 2, Issue 9, 2021

Published on: 14 January, 2021

Article ID: e030821190295 Pages: 16

DOI: 10.2174/2666796702666210114110013

Price: $65

Abstract

Background: Coronavirus disease 2019 (COVID-19) outbreak has created an emergency globally, and social distancing and isolation are the only solution to prevent its spread. Several countries have announced a full lockdown to tackle this pandemic. The coronavirus family is inclusive of pathogens of both- animal species and humans, encapsulating the isolated severe acute respiratory syndrome coronavirus (SARS-CoV). Researchers around the globe have been dexterously working to decode this lethal virus. Many mathematical frameworks have also been depicted, which have helped to understand the dynamics of the COVID-19.

Methods: This systematic review highlights the virus genomic composition, preliminary phylogenetic analysis, pathogenesis, symptomatology, diagnosis, and prognosis along with mathematical models of disease transmission and dynamics.

Results: Our preliminary phylogenetic analysis of the novel coronavirus sequence discerns that although shares its lineage with SARS, BAT-CoV, Beta-BAT-SARS, however, this protein is highly dissimilar to its ancestors. The widely prominent amino acid residues found in the protein are alanine (ALA), aspartic acid (ASP), phenylalanine (PHE), leucine (LEU), aspartic acid (ASP), threonine (THR), valine (VAL), tyrosine (TYR) and asparagine (ASN) that are responsible for its replication process.

Conclusion: Research on coronaviruses continues towards developing a strong understanding of the rapidly evolving viral replication and its transmission between individuals.

Keywords: SARS-CoV-2, COVID-19, coronavirus, viral pandemic, nCoV-19, mathematical modeling.

Graphical Abstract


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