Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia.
Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia.
Popul Health Metr. 2023 Oct 29;21(1):17. doi: 10.1186/s12963-023-00318-6.
The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects.
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. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions.
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.
Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.
由于出现了几种高传染性的关注变种,COVID-19 大流行给世界各地的公共卫生系统带来了压力。过去几年部署的多样化和复杂的干预政策在控制大流行方面显示出不同的效果。然而,由于缺乏衡量大流行不平等和非线性效应的适当措施,对不同病毒谱系和复杂干预政策的综合影响进行系统分析和建模仍然是一个挑战。
使用大规模基于代理的建模和与澳大利亚人口普查人口统计学相匹配的高分辨率计算模拟,我们对几种 COVID-19 大流行情景进行了系统的比较分析。这些情景涵盖了最近的两个澳大利亚人口普查年(2016 年和 2021 年)、三个关注变种(原始、Delta 和奥密克戎)和五个有代表性的干预政策。我们引入了大流行洛伦兹曲线,用于衡量大流行严重程度在当地的不平等分布。我们还量化了大流行双峰性,区分了城市和区域波,并测量了干预措施有效性的分岔。
我们量化了人口异质性对大流行严重程度的非线性影响,强调了(i)人口增长放大了大流行峰值,(ii)人口规模的变化比密度的变化放大了峰值发病率,(iii)大流行严重程度在当地不平等分布。我们还研究并划定了城市化对发病率双峰性的影响,区分了城市和区域大流行波。最后,我们量化并研究了学校关闭的影响,辅以部分干预措施,并确定了包括学校关闭可能决定性控制传播的条件。
对长期大流行的公共卫生应对必须经常审查和适应人口变化。为了控制复发性波,大规模疫苗接种推广需要辅以部分非药物干预措施。医疗保健和疫苗资源需要优先考虑人口增长和/或密度高的地方和地区。