DeepCOVID: An Operational Deep Learning -driven Framework for Explainable Real-time COVID-19 Forecasting
35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence
; 35:15393-15400, 2021.
Article
in English
| Web of Science | ID: covidwho-1436877
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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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.
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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
33rd Conference on Innovative Applications of Artificial Intelligence
Year:
2021
Document Type:
Article
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