Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.
J R Soc Interface. 2022 Aug;19(193):20220123. doi: 10.1098/rsif.2022.0123. Epub 2022 Aug 3.
Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.
及时预测人类传染病的出现、再现和消除，可以做出主动而非被动的决策，从而拯救生命。最近的理论表明，接近临界点的动力系统的一个通用特征——由于临界减速（CSD）而产生的早期预警信号（EWS）——可以预测疾病的出现和消除。记录观察到的疾病动态中 CSD 的实证研究很少，但对概念的这种证明对于进一步开发模型独立的疫情检测系统至关重要。在这里，我们使用尼日尔四个城市的麻疹传播拟合的、基于机制的模型，通过统计 EWS 来检测 CSD。我们发现，几个 EWS 可以准确地预测麻疹的再现和消除，这表明在疾病传播系统越过关键临界点之前，应该可以检测到 CSD。这些发现支持了这样一种观点，即基于 CSD 的统计信号，加上决策支持算法和专家判断，可以为疾病爆发的早期预警系统提供基础。
