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.