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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

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

February 1, 2023

Authors

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


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

IAAI Technical Track on Emerging Applications of AI

Downloads:

Download PDF

Abstract:

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 FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

DOI:

10.1609/aaai.v35i17.17808


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35



Topics: AAAI

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