Computation to Fight SARS-CoV-2 (CoVid-19)

doi:10.3390/books978-3-7258-0047-6

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Hardback

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Keywords

COVID-19; coronavirus; protease; spike protein; computational; inhibition; COVID-19; air pollution; Emilia-Romagna; Granger-causality; time series; correlation; COVID-19; predictions; second wave; machine learning models; air pollution; Emilia- Romagna; Italy; COVID-19; SARS-CoV-2; computational chemistry; structure-based; pharmacophore; docking; MM-GBSA; binding energy; eucalyptus compounds; molecular docking; molecular dynamics; SARS-CoV-2; Twitter; machine learning; causal inference; COVID-19; sentiment analysis; social media; COVID-19; chest X-ray; convolutional neural network; classification; deep learning; COVID-19; machine learning; deep learning; NLP; weather; sentiment analysis; COVID-19; least-squares finite element method; susceptible-exposed-infected-quarantined-recovered-deceased (SEIQRD); daily reproduction number; COVID-19 outbreak; discrete epidemic growth equation; discrete deconvolution; COVID-19 in several countries; COVID-19; topic modeling; BigARTM; latent Dirichlet analysis; mass media analysis; quinadoline B; SARS-CoV-2; RNA-dependent RNA polymerase inhibitors; virtual screening; combinatorial screening; molecular dynamics; deep learning; explainable artificial intelligence; machine learning; mortality; prediction; ventilator support; alpha-7 nicotinic receptor; CD147; docking; ivermectin; molecular modeling; SARS-CoV-2; SARS-CoV-2; main protease (M^pro); computer-aided drug design; molecular docking; molecular dynamics; COVID-19; SEIR models; dynamics generator; unrecorded infections; Richard’s curve; innate immunity; interferon-stimulated genes (ISGs); ISGylation; phytochemicals; PLpro; immunomodulation; bioinformatics; COVID-19; SARS-CoV-2; immunoinformatic; mRNA; vaccine; modeling; computational; traffic congestion; departure delay; COVID-19; traffic delay; commuter perception; chi-square test; fuzzy synthetic evaluation (FSE); COVID-19; vaccines; Twitter; sentiment analysis; classification; machine learning; COVID-19 cases; West Java Province; k-medoids clustering algorithm; shape-based lock step measures; cross the correlation-based distance; COVID-19; topic modeling; latent Dirichlet allocation; machine learning; text mining; dendrogram; infodemic; COVID-19; Google Trends; multivariate analysis; COVID-19; forecasting; flower pollination algorithm; recurrent neural network; coarse-grained modeling; SARS-CoV-2; molecular dynamics; machine learning; SARS-CoV-2 variant; Omicron wave; mathematical modeling; vaccination; scenarios; simulations; social media; COVID-19; psychological impact; social distancing; knowledge; n/a

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Computation to Fight SARS-CoV-2 (CoVid-19)

Edited by

January 2024

544 pages

ISBN 978-3-7258-0048-3 (Hardback)
ISBN 978-3-7258-0047-6 (PDF)

https://doi.org/10.3390/books978-3-7258-0047-6

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This is a Reprint of the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19) that was published in

Biology & Life Sciences

Summary

This is a reprint of articles from the Special Issue published online in the open-access journal Computation (ISSN 2079-3197) titled Computation to Fight SARS-CoV-2 (COVID-19). This reprint contains articles concerning the last pandemic health emergency, considering the period 2020–2023.