# DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions

arXiv: https://arxiv.org/abs/2008.00115 · HTML: https://mlsanity.com/paper/2008.00115/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting](https://mlsanity.com/paper/2109.07438/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study](https://mlsanity.com/paper/2207.05753/) (2022)
- [Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes](https://mlsanity.com/paper/2209.09448/) (2022)
- [Population Age Group Sensitivity for COVID-19 Infections with Deep Learning](https://mlsanity.com/paper/2307.00751/) (2023)
- [Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data](https://mlsanity.com/paper/2109.05087/) (2021)
- [Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models](https://mlsanity.com/paper/2210.03258/) (2022)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)

## Abstract (arXiv)

In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for national-level surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.