Plank M J, Hart W S, Polonsky J, Keita M, Ahuka-Mundeke S, Thompson R N
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Mathematical Institute, University of Oxford, Oxford, UK.
Proc Biol Sci. 2025 Jan;292(2039):20242825. doi: 10.1098/rspb.2024.2825. Epub 2025 Jan 29.
Towards the end of an infectious disease outbreak, when a period has elapsed without new case notifications, a key question for public health policymakers is whether the outbreak can be declared over. This requires the benefits of a declaration (e.g. relaxation of outbreak control measures) to be balanced against the risk of a resurgence in cases. To support this decision-making, mathematical methods have been developed to quantify the end-of-outbreak probability. Here, we propose a new approach to this problem that accounts for a range of features of real-world outbreaks, specifically: (i) incomplete case ascertainment, (ii) reporting delays, (iii) individual heterogeneity in transmissibility and (iv) whether cases were imported or infected locally. We showcase our approach using two case studies: Covid-19 in New Zealand in 2020 and Ebola virus disease in the Democratic Republic of the Congo in 2018. In these examples, we found that the date when the estimated probability of no future infections reached 95% was relatively consistent across a range of modelling assumptions. This suggests that our modelling framework can generate robust quantitative estimates that can be used by policy advisors, alongside other sources of evidence, to inform end-of-outbreak declarations.
在传染病爆发接近尾声时，即经过一段时间没有新病例报告时，公共卫生政策制定者面临的一个关键问题是能否宣布疫情结束。这需要在宣布疫情结束的益处（如放宽疫情控制措施）与病例再次出现的风险之间进行权衡。为支持这一决策过程，已开发出数学方法来量化疫情结束的概率。在此，我们针对这一问题提出一种新方法，该方法考虑了现实世界疫情的一系列特征，具体包括：（i）病例确诊不完整；（ii）报告延迟；（iii）传播性的个体异质性；以及（iv）病例是输入性的还是本地感染的。我们通过两个案例研究展示了我们的方法：2020年新西兰的新冠疫情和2018年刚果民主共和国的埃博拉病毒病疫情。在这些例子中，我们发现，在一系列建模假设下，预计未来无感染概率达到95%的日期相对一致。这表明我们的建模框架能够生成可靠的定量估计，政策顾问可将其与其他证据来源一起用于为宣布疫情结束提供依据。
