The race to understand immunopathology in COVID-19: perspectives on the impact of quantitative approaches to understand within-host interactions

Par Conseil national de recherches du Canada

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DOI	Trouver le DOI : https://doi.org/10.1016/j.immuno.2023.100021
Auteur	Rechercher : Gazeau, Sonia; Rechercher : Deng, Xiaoyan; Rechercher : Ooi, Hsu Kiang¹; Rechercher : Mostefai, Fatima; Rechercher : Hussin, Julie; Rechercher : Heffernan, Jane; Rechercher : Jenner, Adrianne L.; Rechercher : Craig, Morgan Identifiant ORCID : https://orcid.org/0000-0003-4852-4770
Affiliation	Conseil national de recherches du Canada. Technologies numériques
Format	Texte, Article
Sujet	COVID-19; mathematical modelling; within-host dynamics; computational modelling; population genetics; machine learning; immunopathology; SARS-CoV-2
Résumé	The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
Date de publication	2023-01-08
Maison d’édition	Elsevier BV
Déclaration de droit d’auteur	Crown Copyright © 2023 Published by Elsevier B.V.
Licence	Creative Commons, Attribution 4.0 Générique (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/deed.fr
Dans	ImmunoInformatics 9, nº C, 100021 (8 janvier 2023) : 1–12.
Langue	anglais
Publications évaluées par des pairs	Oui
Identificateur	S2667119023000010
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	3cc914ae-2d21-4a71-9ca1-5fb95af5a5b6
Enregistrement créé	2023-03-06
Enregistrement modifié	2023-03-06

Date de modification :: 2024-07-18