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Journal Article Review Ppublish 2 citations

PubMed: 36643886 DOI: 10.1016/j.immuno.2023.100021

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

Immunoinformatics (Amst) 9:100021

Authors: Sonia Gazeau, Xiaoyan Deng, Hsu Kiang Ooi, Fatima Mostefai, Julie Hussin, Jane Heffernan, Adrianne L Jenner, Morgan Craig

Affiliations

  • Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada.
  • Sainte-Justine University Hospital Research Centre, Montréal, Canada.
  • Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada. Sainte-Justine University Hospital Research Centre, Montréal, Canada.
  • Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada.
  • Montréal Heart Institute Research Centre, Montréal, Canada.
  • Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada.
  • Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada.
  • Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada.
  • School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia.

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.

MeSH

Sars-Cov-2, Covid-19, Pandemics

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