TY - GEN
T1 - DeepCOVID
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
AU - Rodríguez, Alexander
AU - Tabassum, Anika
AU - Cui, Jiaming
AU - Xie, Jiajia
AU - Ho, Javen
AU - Agarwal, Pulak
AU - Adhikari, Bijaya
AU - Prakash, B. Aditya
N1 - Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85102342229&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85102342229
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 15393
EP - 15400
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
PB - Association for the Advancement of Artificial Intelligence
Y2 - 2 February 2021 through 9 February 2021
ER -