COVID-19 in Australia: Systems resilience and outcome fairness

We apply a relational multiscalar model of the sustainability of social organisation to the management of COVID-19 in Australia to analyse systems resilience and outcome fairness. Our model encompasses the structures, processes, functions, and contents of social organisation across micro, meso, and macro scales. We developed four data sets linked to COVID-19 and analysed these as variables in our model: (1) letters to the editor in public newspapers; (2) net weekly payroll jobs growth; (3) income/wealth inequality; (4) COVID-19 mortality and case/infection rates. These variables are used as proxies for democratic systems resilience, economic systems resilience, socioeconomic systems fairness, and public health systems efficacy, respectively. We found that as the pandemic progressed, public opinion shifted from favouring structural-societal transformation to favouring incremental adaptation. Spatial scale and geographical location impacted the resilience of weekly payroll jobs, with ‘urban’/densely populated areas having less job growth than ‘regional’/less dense locations, and ‘national’ scale net jobs growth being greater than ‘job growth in New South Wales, Tasmania, and Victoria, in decreasing order. Transitions in pandemic policies of border restrictions and vaccination constrained net job losses, or enabled net gains over time, with the federal JobKeeper wage support preventing ‘breakout’ unemployment. Mobility restrictions and vaccination minimised mortality rates with self-administered (RAT) testing possibly decreasing infection rates. While our findings affirm that the complexity and ‘messiness’ of social policy means that management outcomes are not easily predictable nor will necessarily match expectations, our model provides a framework for assessing system dynamics and outcome fairness.