Ogi-Gittins I, Polonsky J, Keita M, Ahuka-Mundeke S, Hart W S, Plank M J, Lambert B, Hill E M, Thompson R N
Mathematics Institute, University of Warwick, Coventry, UK.
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK.
Infect Dis Model. 2025 Apr 1;10(3):935-945. doi: 10.1016/j.idm.2025.03.009. eCollection 2025 Sep.
Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks.
皮埃尔·马加尔教授在2024年2月20日去世前，对数学生物学领域做出了重要贡献，包括利用流行病学模型研究传染病爆发末期的研究。在相关工作中，人们一直对实时推断疫情何时结束以及控制干预措施何时可以放松感兴趣。在此，我们分析了刚果民主共和国赤道省2018年埃博拉疫情的数据，在此期间部署了埃博拉应对小组（ERT）以实施公共卫生措施。我们使用更新方程传播模型对疫情末期ERT何时可以安全撤离进行实时调查。具体而言，在ERT抵达后的每周，我们计算如果ERT撤离，未来出现病例的概率。首先，我们表明从每日或每周病例报告中都可以获得类似的未来病例概率估计值。这表明，在确定何时可以放松干预措施时，可能并不总是需要高时间分辨率的病例报告。其次，我们展示了在估计未来病例概率时如何严格考虑病例漏报情况。我们发现，病例报告水平越低，在明显的最后一例病例之后，就需要等待更长时间才能安全地取消干预措施（出现额外病例的概率很小）。最后，我们展示了如何将病例报告范围的不确定性纳入未来病例概率的估计中。我们的研究强调了在决定何时在传染病爆发末期取消干预措施时考虑漏报情况的重要性。
