# A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

arXiv: https://arxiv.org/abs/2109.12094 · HTML: https://mlsanity.com/paper/2109.12094/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 117 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [Neural network based country wise risk prediction of COVID-19](https://mlsanity.com/paper/2004.00959/) (2020)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model](https://mlsanity.com/paper/2006.05581/) (2020)
- [The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook](https://mlsanity.com/paper/2004.03055/) (2020)

## Built on by (most cited first, 7 of 7)

- [Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study](https://mlsanity.com/paper/2305.07731/) (2023)
- [Performance and Generalizability Impacts of Incorporating Location Encoders into Deep Learning for Dynamic PM2.5 Estimation](https://mlsanity.com/paper/2505.18461/) (2025)
- [Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes](https://mlsanity.com/paper/2209.09448/) (2022)
- [Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data](https://mlsanity.com/paper/2310.14549/) (2023)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)
- [DemOpts: Fairness corrections in COVID-19 case prediction models](https://mlsanity.com/paper/2405.09483/) (2024)
- [Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting](https://mlsanity.com/paper/2506.05752/) (2025)

## Abstract (arXiv)

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper we approach the forecasting task with an alternative technique - spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a Long Short-term Memory deep learning architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub's Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.