Evidence map›Paper›PMID 39809333›Full record

ArticleJournal of the Royal Society, Interface2025

Adaptive behaviour during epidemics: a social risk appraisal approach to modelling dynamics.

David O'Gara, Matt Kasman, Laurent Hébert-Dufresne, Ross A Hammond

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

David O'GaraDivision of Computational and Data Sciences, Washington University in St Louis, One Brookings Drive, St Louis, MO 63105, USA.ORCID 0000-0002-1957-400X
Matt KasmanCenter on Social Dynamics and Policy, Brookings Institution, 1775 Massachusetts Avenue NW, Washington, DC 20036, USA.ORCID 0000-0002-0860-3442
Laurent Hébert-DufresneVermont Complex Systems Center, University of Vermont, 82 University Place, Burlington, VT 05405, USA.ORCID 0000-0002-0008-3673
Ross A HammondDivision of Computational and Data Sciences, Washington University in St Louis, One Brookings Drive, St Louis, MO 63105, USA.ORCID 0009-0005-1046-9296

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
National Institute of Food and AgricultureNIGMS NIH HHS P20 GM125498NIH HHSWashington University in St. Louis
6 · The paper itself

Abstract

The interaction of infectious diseases and behavioural responses to them has been the subject of widespread study. However, limited attention has been given to how broader social context shapes behavioural response. In this work, we propose a novel framework which combines two well-studied dynamic processes into a 'social risk appraisal' mechanism. Our proposed framework has both theoretical and empirical support, occupying an important middle ground in the interacting contagions literature. Results indicate that a risk appraisal framework can express a wide range of epidemic outcomes, driven by simple interaction rules. This framework has implications for designing containment strategies in disease outbreaks, as well as equity considerations. Finally, the risk appraisal approach is well-posed to engage with a broad set of literature in epidemic management, decision-making and the adoption of social behaviours.

Indexed as

Communicable DiseasesEpidemicsModels, BiologicalSocial BehaviorHumansbehavioural responsecomputational epidemiologycontagion

Identifiers

PMID39809333
PMCPMC11732433

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.