  Diverse and complex intervention policies deployed over the last years have
shown varied effectiveness in controlling the COVID-19 pandemic. However, a
systematic analysis and modelling of the combined effects of different viral
lineages and complex intervention policies remains a challenge. Using
large-scale agent-based modelling and a high-resolution computational
simulation matching census-based demographics of Australia, we carried out a
systematic comparative analysis of several COVID-19 pandemic scenarios. The
scenarios covered two most recent Australian census years (2016 and 2021),
three variants of concern (ancestral, Delta and Omicron), and five
representative intervention policies. In addition, we introduced pandemic
Lorenz curves measuring an unequal distribution of the pandemic severity across
local areas. We quantified nonlinear effects of population heterogeneity on the
pandemic severity, highlighting that (i) the population growth amplifies
pandemic peaks, (ii) the changes in population size amplify the peak incidence
more than the changes in density, and (iii) the pandemic severity is
distributed unequally across local areas. We also examined and delineated the
effects of urbanisation on the incidence bimodality, distinguishing between
urban and regional pandemic waves. Finally, we quantified and examined the
impact of school closures, complemented by partial interventions, and
identified the conditions when inclusion of school closures may decisively
control the transmission. Our results suggest that (a) public health response
to long-lasting pandemics must be frequently reviewed and adapted to
demographic changes, (b) in order to control recurrent waves, mass-vaccination
rollouts need to be complemented by partial NPIs, and (c) healthcare and
vaccination resources need to be prioritised towards the localities and regions
with high population growth and/or high density.
