DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting


  • Alexander Rodríguez Georgia Institute of Technology
  • Anika Tabassum Virginia Tech
  • Jiaming Cui Georgia Institute of Technology
  • Jiajia Xie Georgia Institute of Technology
  • Javen Ho Georgia Institute of Technology
  • Pulak Agarwal Georgia Institute of Technology
  • Bijaya Adhikari University of Iowa
  • B. Aditya Prakash Georgia Institute of Technology


Epidemic Forecasting, COVID-19, Deep Learning, Artificial Intelligence For Social Good


How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.




How to Cite

Rodríguez, A., Tabassum, A., Cui, J., Xie, J., Ho, J., Agarwal, P., Adhikari, B., & Prakash, B. A. (2021). DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15393-15400. Retrieved from



IAAI Technical Track on Emerging Applications of AI