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