Evidence map›Paper›PMID 41017901›Full record

ArticleInterface focus2025

Impact of opinion dynamics on recurrent pandemic waves: balancing risk aversion and peer pressure.

Sheryl L Chang, Quang Dang Nguyen, Carl Joseph Edmund Suster, Ma Christina Jamerlan, Rebecca J Rockett, Vitali Sintchenko, Tania C Sorrell, Alexandra Martiniuk, Mikhail Prokopenko

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Interface focus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Sheryl L ChangCentre for Complex Systems, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-6119-828X
Quang Dang NguyenCentre for Complex Systems, The University of Sydney, Sydney, New South Wales, Australia.
Carl Joseph Edmund SusterSydney Infectious Diseases Institute, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-7021-9380
Ma Christina JamerlanCentre for Complex Systems, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0003-8998-2580
Rebecca J RockettSydney Infectious Diseases Institute, The University of Sydney, Sydney, New South Wales, Australia.
Vitali SintchenkoSydney Infectious Diseases Institute, The University of Sydney, Sydney, New South Wales, Australia.
Tania C SorrellSydney Infectious Diseases Institute, The University of Sydney, Sydney, New South Wales, Australia.
Alexandra MartiniukFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Mikhail ProkopenkoCentre for Complex Systems, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-4215-0344

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recurrent waves, which are often observed during long pandemics, typically form as a result of several interrelated dynamics, including public health interventions, population mobility and behaviour, varying disease transmissibility due to pathogen mutations, and changes in host immunity due to recency of vaccination or previous infections. Complex nonlinear dependencies among these dynamics, including feedback between disease incidence and the opinion-driven adoption of social distancing (SD) behaviour, remain poorly understood, particularly in scenarios involving heterogeneous population, partial and waning immunity and rapidly changing public opinions. This study addressed this challenge by proposing an opinion dynamics model that accounts for changes in SD behaviour (i.e. whether to adopt SD) by modelling both individual risk perception and peer pressure. The opinion dynamics model was integrated and validated within a large-scale agent-based COVID-19 pandemic simulation that modelled the spread of the Omicron variant of SARS-CoV-2 between December 2021 and June 2022 in Australia. Our study revealed that while holding epidemiological factors constant, the fluctuating adoption of SD, shaped by individual risk aversion and social peer pressure from both household and workplace environments, can reproduce these multi-wave patterns, pointing to the importance of social dynamics in understanding epidemic outcomes.

Indexed as

COVID-19infectious diseaseopinion dynamicspandemic modellingrecurrent wavessocial distancing

Identifiers

PMID41017901
PMCPMC12464575

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.