ABSTRACT

Between February and May 2020, New Zealand recorded 1504 cases of COVID-19 before eliminating community transmission of the virus in June 2020. During this period, a series of control measures were used including population-wide interventions implemented via a four-level alert system, border restrictions, and a test, trace, and isolate system. Mathematical modelling played a key role in informing the government response and guiding policy development. In this paper, we describe the development of a stochastic mathematical model for the transmission and control of COVID-19 in New Zealand. This includes features such as superspreading, case under-ascertainment, testing and reporting delays, and population-wide and case-targeted control measures. We show how the model was calibrated to New Zealand and international data. We describe how the model was used to compare the effects of various interventions in reducing spread of the virus and to estimate the probability of elimination. We conclude with a discussion of the policy-modelling interface and preparedness for future epidemic outbreaks.

摘要

在2020年2月至2020年5月之间，新西兰记录了1504例COVID-19病例，然后在2020年6月消除了社区传播的病毒。在此期间，采取了一系列控制措施，包括通过四级警报系统实施的全民干预。 ，边界限制以及测试，跟踪和隔离系统。数学建模在通知政府反应和指导政策制定方面发挥了关键作用。在本文中，我们描述了在新西兰传播和控制COVID-19的随机数学模型的开发。这包括诸如超级传播，病例不足确定，测试和报告延迟以及人群范围和针对病例的控制措施等功能。我们展示了如何根据新西兰和国际数据对模型进行校准。我们描述了该模型如何用于比较各种干预措施在减少病毒传播方面的效果以及估计消除的可能性。最后，我们讨论了政策模型的接口和对未来流行病爆发的准备。