Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods.

翻译：最近,疾病控制和预防中心(疾病控制和预防中心)与其他联邦机构合作,确定2019年冠状病毒(COVID-19)发病率(热点)上升的各州,并向地方卫生部门提供支持,以限制该疾病的传播。了解热点事件的时空动态对于支持决策和防止大规模爆发非常重要。本文展示了一个早期发现美国COVID-19热点(县一级)的时空贝叶斯框架。我们假设观察到的病例数量和热点都取决于一组潜在的随机变量,这些变量将COVID-19传播的潜在瞬时动态进行编码。这些潜在变量遵循一种零度高温过程,其共性由非静止内核功能确定。我们内核功能的最突出特征是引入深层神经网络,以加强模型的代表性力量,同时仍然享有模型内核的可解释性。我们用一种稀有的模型模型和高温点模型的可变化性,我们用一种更隐蔽的模型来比较高温度的模型,我们用一个更隐蔽的模型来学习高压的模型。