Social influencers reduce infection burden and modify epidemic lag in group-structured populations

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.