Sponsor
Partial support for this research came from a Hoffman-Yee Grant from the Stanford Center for Human-Centered AI (HAI), Stanford University.
Published In
Royal Society Open Science
Document Type
Article
Publication Date
6-2-2026
Subjects
Social epidemiology, infectious disease, agent-based modelling, coupled contagion, digital culture, social media
Abstract
The impact of online social influence on social learning, health behaviour and population health is a new, rapidly developing area of study. Previous research focused specifically on digital influencers suggests they may be able to affect group-level diffusion of health-protective behaviours such that they modify epidemic outcomes. However, formal models have yet to test the intuitive hypothesis that the effect of digital influencers is sufficient to generate tangible real-world effects on epidemics. We develop an agent-based model that incorporates digital influencers into an epidemic scenario to test hypotheses about how competing influence messages affect the diffusion of health-protective behaviours throughout a population and thereby alter the course and outcome of infectious disease epidemics. We find influencers had a persistent independent effect on peak infection intensity and total infection burden of the epidemic, with the greatest effects in highly homophilous scenarios. The presence of health-protective influence effectively flattened the epidemic curve despite equal presence of anti-protective influence.
Rights
Copyright (c) 2026 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1098/rsos.251603p
Persistent Identifier
https://archives.pdx.edu/ds/psu/44745
Publisher
The Royal Society
Citation Details
Sutton, A., Reynolds, A. Z., Turner, M. A., & Jones, J. H. (2026). Social influencers reduce infection burden and modify epidemic lag in group-structured populations. Royal Society Open Science, 13(6).