# A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19

arXiv: https://arxiv.org/abs/2007.10929 · HTML: https://mlsanity.com/paper/2007.10929/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [A data augmentation strategy for deep neural networks with application to epidemic modelling](https://mlsanity.com/paper/2502.21033/) (2025)
- [Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy](https://mlsanity.com/paper/2510.09192/) (2025)
- [Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19](https://mlsanity.com/paper/2103.11860/) (2021)
- [An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics](https://mlsanity.com/paper/2201.09394/) (2022)

## Abstract (arXiv)

The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The latter captures latent spatial information. %that is not publicly reported. We trained and tested our model on COVID-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting especially in the regime of limited training data.