论文标题
动力学的反趋义神经网络，用于流行病学时间序列中政权变化的超分辨率识别
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series
论文作者
论文摘要
从其随机传播低分辨率的结果中，推断流行病学系统中扰动的时间和幅度与具有挑战性一样重要。这是当前方法的要求，以克服了解扰动的细节以进行分析的细节。但是，将流行病学曲线与潜在发病率连接起来的总体问题缺乏其他反问题中存在的高效方法，例如超分辨率和从计算机视觉中脱颖而出。在这里，我们开发了一种无监督的物理知识的卷积神经网络方法，以相反，以将死亡记录与发病率联系起来，以识别单日解决方案的政权变化。该方法适用于适当的正规化和模型选择标准的COVID-19数据，可以在一年的时间范围内确定锁定和其他非药物干预措施的实施和删除，并确定其他非药物干预措施。
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.
