Abstract
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.
Original language | English (US) |
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Pages (from-to) | 250-260 |
Number of pages | 11 |
Journal | IEEE Journal on Selected Topics in Signal Processing |
Volume | 16 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2007-2012 IEEE.
Keywords
- COVID-19 hotspots
- Gaussian processes
- non-stationary kernel
- spatio-temporal model