Adaptive short term COVID-19 prediction for India

  1. Shuvrangshu Jana
  2. Debasish Ghose

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Abstract

In this paper, a data-driven adaptive model for infection of COVID-19 is formulated to predict the confirmed total cases and active cases of an area over 4 weeks. The parameter of the model is always updated based on daily observations. It is found that the short term prediction of up to 3-4 weeks can be possible with good accuracy. Detailed analysis of predicted value and the actual value of confirmed total cases and active cases for India from 1 st June to 3 rd July is provided. Prediction over 7, 14, 21, 28 days has the accuracy about 0.73% ± 1.97%, 1.92% ± 2.95%, 4.34% ± 3.91%, 6.40% ± 9.26% of the actual value of confirmed total cases. Similarly, the 7, 14, 21, 28 days prediction has the accuracy about 1.24% ± 6.57%, 3.04% ± 10.00%, 6.33% ± 16.12%, 10.20% ± 24.14% of the actual value of confirmed active cases.

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    SciScore for 10.1101/2020.07.18.20156745: (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.

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  2. Version published to 10.1101/2020.07.18.20156745v1 on medRxiv