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Diverse and complex intervention policies deployed over the last years haveshown varied effectiveness in controlling the COVID-19 pandemic. However, asystematic analysis and modelling of the combined effects of different virallineages and complex intervention policies remains a challenge. Usinglarge-scale agent-based modelling and a high-resolution computationalsimulation matching census-based demographics of Australia, we carried out asystematic comparative analysis of several COVID-19 pandemic scenarios. Thescenarios covered two most recent Australian census years (2016 and 2021),three variants of concern (ancestral, Delta and Omicron), and fiverepresentative intervention policies. In addition, we introduced pandemicLorenz curves measuring an unequal distribution of the pandemic severity acrosslocal areas. We quantified nonlinear effects of population heterogeneity on thepandemic severity, highlighting that (i) the population growth amplifiespandemic peaks, (ii) the changes in population size amplify the peak incidencemore than the changes in density, and (iii) the pandemic severity isdistributed unequally across local areas. We also examined and delineated theeffects of urbanisation on the incidence bimodality, distinguishing betweenurban and regional pandemic waves. Finally, we quantified and examined theimpact of school closures, complemented by partial interventions, andidentified the conditions when inclusion of school closures may decisivelycontrol the transmission. Our results suggest that (a) public health responseto long-lasting pandemics must be frequently reviewed and adapted todemographic changes, (b) in order to control recurrent waves, mass-vaccinationrollouts need to be complemented by partial NPIs, and (c) healthcare andvaccination resources need to be prioritised towards the localities and regionswith high population growth and/or high density.

Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions