用于流行病学时间序列中状态变化超分辨率识别的动力学信息反卷积神经网络
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series
AI总结：
提出一种无监督物理信息反卷积 CNN，将死亡记录逆向映射到发病率，实现单日分辨率的流行病学状态变化识别，并在 COVID-19 数据上以 0.93 天精度定位非药物干预。
摘要：
从流行病学系统随机传播的低分辨率观测结果中推断扰动的发生时间和幅度，既具有现实意义，也颇具挑战性。当前方法若要开展分析，必须先克服掌握扰动细节这一需求。然而，将流行病学曲线与潜在发病率关联起来这一普遍问题，尚缺乏计算机视觉中超分辨率和去雾等其他逆问题所具备的高效方法。为此，我们以逆向方式开发了一种无监督的物理信息卷积神经网络方法，用于将死亡记录与发病率关联起来，从而能够以单日分辨率识别状态变化。将该方法应用于 COVID-19 数据，并采用适当的正则化和模型选择准则，可在一年的时间跨度内以 0.93 天的精度识别封锁及其他非药物干预措施的实施与解除。
英文摘要：
Inferring the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is as relevant as challenging. It is a requirement for current approaches to overcome the need to know the details of the perturbations to proceed with the analyses. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions with 0.93-day accuracy over the time span of a year.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
