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

  1. Alexander Rodríguez
  2. Anika Tabassum
  3. Jiaming Cui
  4. Jiajia Xie
  5. Javen Ho
  6. Pulak Agarwal
  7. Bijaya Adhikari
  8. B. Aditya Prakash

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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 D eep C ovid , an operational deep learning frame-work designed for real-time COVID-19 forecasting. D eep -C ovid 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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  1. Version published to 10.1101/2020.09.28.20203109v3 on medRxiv
  2. Version published to 10.1101/2020.09.28.20203109v2 on medRxiv
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    SciScore for 10.1101/2020.09.28.20203109: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

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    Results from JetFighter: We did not find any issues relating to colormaps.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.09.28.20203109v1 on medRxiv