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:
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