Delecroix Clara, van Nes Egbert H, van de Leemput Ingrid A, Rotbarth Ronny, Scheffer Marten, Ten Bosch Quirine
Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands.
Quantitative Veterinary Epidemiology, Wageningen University, Wageningen, The Netherlands.
PLOS Glob Public Health. 2023 Oct 10;3(10):e0002253. doi: 10.1371/journal.pgph.0002253. eCollection 2023.
To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.
为降低传染病暴发的后果，及时实施公共卫生措施至关重要。目前使用的早期预警系统高度依赖具体情况，且需要较长的模型构建阶段。一种预测暴发开始或结束的提议解决方案是使用所谓的恢复力指标。这些指标基于临界减缓的一般理论，只需要发病率时间序列。在此，我们评估这种方法对暴发预测的贡献潜力。我们系统回顾了使用恢复力指标预测疫情暴发或结束的研究。我们确定了37项符合纳入标准的研究：21项使用模拟数据，16项使用真实世界数据。37项研究中有36项在临界转变（即暴发开始或结束）之前检测到了临界减缓的显著迹象，灵敏度（即真正的阳性暴发预警比例）变化很大，范围从0.03到1，提前期从10天到68个月不等。挑战包括时间序列分辨率低和长度有限、病例增加过快以及强烈的季节性模式，这些可能会妨碍恢复力指标的灵敏度。在某些情况下，谷歌搜索或社交媒体数据等其他类型的数据有可能改善预测。当疾病暴发风险逐渐变化时，恢复力指标可能会有用。例如，当病原体越来越适应环境或逐渐进化以逃避免疫时，就可能发生这种情况。需要进行高分辨率监测以达到足够的灵敏度。如果满足这些条件，恢复力指标有助于改进当前的预测实践，促进及时的暴发应对。我们提供了一份在传染病流行病学中使用恢复力指标的分步指南，以及关于使用这种方法的相关情况的指导。
