School of Mathematics and Statistics, University of Canterbury, Science Road Christchurch 8140, New Zealand and Te Pūnaha Matatini, University of Auckland, 38 Princes Street Auckland 1010, New Zealand.
Manaaki Whenua, 54 Gerald Street, Lincoln 7608, New Zealand and Te Pūnaha Matatini, University of Auckland, 38 Princes Street Auckland 1010, New Zealand.
Math Med Biol. 2021 Aug 15;38(3):299-313. doi: 10.1093/imammb/dqab006.
We use a stochastic branching process model, structured by age and level of healthcare access, to look at the heterogeneous spread of COVID-19 within a population. We examine the effect of control scenarios targeted at particular groups, such as school closures or social distancing by older people. Although we currently lack detailed empirical data about contact and infection rates between age groups and groups with different levels of healthcare access within New Zealand, these scenarios illustrate how such evidence could be used to inform specific interventions. We find that an increase in the transmission rates among children from reopening schools is unlikely to significantly increase the number of cases, unless this is accompanied by a change in adult behaviour. We also find that there is a risk of undetected outbreaks occurring in communities that have low access to healthcare and that are socially isolated from more privileged communities. The greater the degree of inequity and extent of social segregation, the longer it will take before any outbreaks are detected. A well-established evidence for health inequities, particularly in accessing primary healthcare and testing, indicates that Māori and Pacific peoples are at a higher risk of undetected outbreaks in Aotearoa New Zealand. This highlights the importance of ensuring that community needs for access to healthcare, including early proactive testing, rapid contact tracing and the ability to isolate, are being met equitably. Finally, these scenarios illustrate how information concerning contact and infection rates across different demographic groups may be useful in informing specific policy interventions.
我们使用由年龄和医疗保健水平结构的随机分支过程模型，研究 COVID-19 在人群中的异质传播。我们研究了针对特定群体（例如关闭学校或老年人保持社交距离）的控制方案的效果。尽管我们目前缺乏有关新西兰内部不同年龄组和不同医疗保健水平组之间的接触和感染率的详细经验数据，但这些方案说明了如何利用此类证据来为特定干预措施提供信息。我们发现，除非成年人的行为发生变化，否则重新开放学校会增加儿童之间的传播率，不太可能会显著增加病例数。我们还发现，医疗保健机会有限且与更富裕社区隔离的社区中存在发生未被发现的暴发的风险。不平等程度和社会隔离程度越大，发现任何暴发所需的时间就越长。有充分的证据表明卫生不平等现象，尤其是在获得初级保健和检测方面，毛利人和太平洋岛民在新西兰更容易出现未被发现的暴发。这突显了确保社区获得医疗保健的需求得到公平满足的重要性，包括早期主动检测、快速接触者追踪以及隔离的能力。最后，这些方案说明了有关不同人群之间接触和感染率的信息如何有助于为特定政策干预措施提供信息。
