Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

  1. Chiou-Jye Huang
  2. Yung-Hsiang Chen
  3. Yuxuan Ma
  4. Ping-Huan Kuo

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Abstract

COVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall efficacies of different algorithms, the indicators of mean absolute error and root mean square error were applied in the experiment of this study. The experiment results indicated that compared with other deep learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also verified in this study.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.23.20041608: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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

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

    About SciScore

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  2. Version published to 10.1101/2020.03.23.20041608v1 on medRxiv