Evidence map›Paper›PMID 34678024›Full record

ArticlePhysical review letters2021

Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks.

Guillaume St-Onge, Hanlin Sun, Antoine Allard, Laurent Hébert-Dufresne, Ginestra Bianconi

Abstract read
In one paragraph

Article in Physical review letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Article
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  8. Nonlinear bias toward complex contagion in uncertain transmission settings.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Dynamics on higher-order networks: a review.Journal of the Royal Society, Interface · 2022
    Review
  14. Article
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

5 authors.

Guillaume St-OngeDépartement de physique, de génie physique et d'optique, Université Laval, Québec (Québec) G1V 0A6, Canada.
Hanlin SunSchool of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.
Antoine AllardDépartement de physique, de génie physique et d'optique, Université Laval, Québec (Québec) G1V 0A6, Canada.
Laurent Hébert-DufresneDépartement de physique, de génie physique et d'optique, Université Laval, Québec (Québec) G1V 0A6, Canada.
Ginestra BianconiSchool of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.

Funding

Using Dengue Controlled Human Infection Model to Identify Adaptive Immune Correlates of ProtectionP20GM125498 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Kristen Pierce · 2018 to 2026
$24.9M
NIGMS NIH HHS P20 GM125498
6 · The paper itself

Abstract

The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.

Identifiers

PMID34678024
PMCPMC9199393

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Registered trials

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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 OpenQuestion graph.