# Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

arXiv: https://arxiv.org/abs/2106.00072 · HTML: https://mlsanity.com/paper/2106.00072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 6 of 6)

- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Curating a COVID-19 data repository and forecasting county-level death counts in the United States](https://mlsanity.com/paper/2005.07882/) (2020)
- [Topological Data Analysis of Spatial Systems](https://mlsanity.com/paper/2104.00720/) (2021)

## Built on by (most cited first, 3 of 3)

- [Counterfactual Generative Models for Time-Varying Treatments](https://mlsanity.com/paper/2305.15742/) (2023)
- [Sequential Change Point Detection via Denoising Score Matching](https://mlsanity.com/paper/2501.12667/) (2025)
- [A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior](https://mlsanity.com/paper/2411.04397/) (2024)

## Abstract (arXiv)

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.