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

From National Research Council Canada

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DOI	Resolve DOI: https://doi.org/10.1016/j.immuno.2023.100021
Author	Search for: Gazeau, Sonia; Search for: Deng, Xiaoyan; Search for: Ooi, Hsu Kiang¹; Search for: Mostefai, Fatima; Search for: Hussin, Julie; Search for: Heffernan, Jane; Search for: Jenner, Adrianne L.; Search for: Craig, Morgan ORCID identifier: https://orcid.org/0000-0003-4852-4770
Affiliation	National Research Council of Canada. Digital Technologies
Format	Text, Article
Subject	COVID-19; mathematical modelling; within-host dynamics; computational modelling; population genetics; machine learning; immunopathology; SARS-CoV-2
Abstract	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.
Publication date	2023-01-08
Publisher	Elsevier BV
Copyright statement	Crown Copyright © 2023 Published by Elsevier B.V.
Licence	Creative Commons, Attribution 4.0 Generic (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/
In	ImmunoInformatics 9, no. C, 100021 (8 January 2023): 1–12.
Language	English
Peer reviewed	Yes
Identifier	S2667119023000010
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Record identifier	3cc914ae-2d21-4a71-9ca1-5fb95af5a5b6
Record created	2023-03-06
Record modified	2023-03-06

Date modified:: 2024-10-21