Title: Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions Show Chinese title

Title: 测量疫情严重程度在人口普查年份、关注变体和干预措施之间的不平等分布

Abstract: 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. Show Chinese abstract

Abstract: 近年来部署的多样且复杂的干预政策在控制新冠疫情方面表现出不同的效果。 然而，对不同病毒株和复杂干预政策综合效应的系统分析和建模仍然是一个挑战。 我们使用大规模基于代理的建模和与澳大利亚人口普查数据相匹配的高分辨率计算模拟，对几种新冠疫情情景进行了系统的比较分析。 这些情景涵盖了澳大利亚最近两个普查年份（2016年和2021年）、三种关注变体（原始毒株、德尔塔和奥密克戎），以及五种代表性的干预政策。 此外，我们引入了衡量疫情严重程度在本地地区不平等分布的疫情洛伦兹曲线。 我们量化了人口异质性对疫情严重程度的非线性影响，强调了以下几点：(i) 人口增长会放大疫情高峰，(ii) 人口规模的变化比人口密度的变化更显著地放大峰值发病率，(iii) 疫情严重程度在本地地区分布不均。 我们还研究并划分了城市化对发病率双峰性的效应，区分了城市和区域的疫情浪潮。 最后，我们量化并研究了学校关闭的影响，并辅以部分干预措施，确定了在学校关闭纳入后可能决定性控制传播的条件。 我们的结果表明，(a) 长期大流行病的公共卫生应对措施必须经常审查并适应人口变化，(b) 为了控制反复出现的浪潮，大规模疫苗接种计划需要辅以部分非药物干预措施，(c) 医疗和疫苗资源需要优先分配给人口增长和/或高密度的地区。