The Race to Understand Immunopathology in COVID-19: Perspectives on the Impact of Quantitative Approaches to Understand Within-host Interactions

Overview

Journal Immunoinformatics (Amst)

Date 2023 Jan 16

PMID 36643886

Authors

Sonia Gazeau

Xiaoyan Deng

Hsu Kiang Ooi

Fatima Mostefai

Julie Hussin

Jane Heffernan

Adrianne L Jenner

Morgan Craig

Affiliations

Soon will be listed here.

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.

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