Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.

中文翻译：

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动态足迹能够检测疾病的出现。


开发预测传染病出现或再次出现的方法既重要又及时；然而，传统的基于模型的方法受到潜在驱动因素的不确定性的阻碍。在这里，我们展示了一种可操作的、与机制无关的疾病（重新）出现检测算法，该算法基于源自临界减速理论的早期预警信号（EWS）。具体来说，我们使用计算机模拟来训练监督学习算法，以检测流行病学数据中存在的（重新）出现的动态足迹。然后，我们的算法面临的挑战是预测 2000 年代中期英国腮腺炎和 1980 年后美国百日咳缓慢显现、空间复制的重新出现。我们的方法成功地提前 4 年预测了流行性腮腺炎的复发，在此期间可以采取缓解措施。从 1980 年起，我们的模型以越来越高的准确度识别复苏状态，从而从 1992 年开始进行可靠的分类。此外，我们成功地将检测算法应用于 2 个媒介传播的案例研究，即波多黎各登革热血清型的爆发和迅速展开的登革热血清型病例研究。 2017年马达加斯加爆发鼠疫。总而言之，这些发现说明了理论上的机器学习技术在开发传染病（重新）出现的早期预警系统方面的力量。