2020 DeepcovidAnOperationalDeepLearn

• (Rodriguez et al., 2020) ⇒ Alexander Rodriguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, and B Aditya Prakash. (2020). “Deepcovid: An Operational Deep Learning-driven Framework for Explainable Real-time Covid-19 Forecasting.” In: medRxiv.

Subject Headings: Epidemiological Metric, Epidemiological Forecasting, COVID-19 Forecasting.

Cited By

• http://scholar.google.com/scholar?q=%222020%22+Deepcovid%3A+An+Operational+Deep+Learning-driven+Framework+for+Explainable+Real-time+Covid-19+Forecasting

Quotes

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. Deep-Covid 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 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2020 DeepcovidAnOperationalDeepLearn	Alexander Rodriguez Anika Tabassum Jiaming Cui Jiajia Xie Javen Ho Pulak Agarwal Bijaya Adhikari B Aditya Prakash			Deepcovid: An Operational Deep Learning-driven Framework for Explainable Real-time Covid-19 Forecasting