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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动态足迹可以检测疾病的出现。

为预测传染病的出现或再次出现而开发方法既重要又及时；但是，传统的基于模型的方法因潜在驱动因素周围的不确定性而受阻。在这里，我们展示了一种基于紧急减速理论的预警信号（EWS），是一种针对疾病（重新）发生的，与机制无关的操作检测算法。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出现的（重新）出现的动态足迹。然后，我们的算法面临挑战，以预测2000年代中期英格兰流行性腮腺炎的缓慢表现，在空间上复制的重现以及美国1980年后的百日咳。我们的方法成功地将腮腺炎提前了4年，在此期间可以采取缓解措施。从1980年开始，我们的模型以更高的精度识别出复发状态，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种媒介传播的案例研究，即波多黎各的登革热血清型暴发和迅速展开2017年在马达加斯加爆发鼠疫。综上所述，这些发现说明了理论上讲得通的机器学习技术为开发（重新）传染性疾病预警系统的力量。我们成功地将检测算法应用于2种媒介传播的案例研究，即波多黎各爆发登革热血清型和2017年在马达加斯加迅速爆发的鼠疫。综上所述，这些发现说明了理论上讲得通的机器学习技术为开发（重新）传染性疾病预警系统的力量。我们成功地将检测算法应用于2种媒介传播的案例研究，即波多黎各爆发登革热血清型和2017年在马达加斯加迅速爆发的鼠疫。综上所述，这些发现说明了理论上讲得通的机器学习技术为开发（重新）传染性疾病预警系统的力量。