Evidence map›Paper›PMID 36643886›Full record

ReviewImmunoinformatics (Amsterdam, Netherlands)2023

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

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

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Immunoinformatics (Amsterdam, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Sonia GazeauDepartment of Mathematics and Statistics, Université de Montréal, Montréal, Canada.
Xiaoyan DengDepartment of Mathematics and Statistics, Université de Montréal, Montréal, Canada.
Hsu Kiang OoiDigital Technologies Research Centre, National Research Council Canada, Toronto, Canada.
Fatima MostefaiMontréal Heart Institute Research Centre, Montréal, Canada.
Julie HussinMontréal Heart Institute Research Centre, Montréal, Canada.
Jane HeffernanModelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada.
Adrianne L JennerSchool of Mathematical Sciences, Queensland University of Technology, Brisbane Australia.
Morgan CraigDepartment of Mathematics and Statistics, Université de Montréal, Montréal, Canada.
Centre Hospitalier Universitaire Sainte-Justine · CAMontreal Heart Institute · CANational Research Council Canada · CAQueensland University of Technology · AUYork University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computational modellingCOVID-19ImmunopathologyMachine learningMathematical modellingPopulation geneticsSARS-CoV-2Within-host dynamics

Identifiers

PMID36643886
PMCPMC9826539
OpenAlexW4313731306

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.