  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.
