A data-driven method to detect the flattening of the COVID-19 pandemic curve and estimating its ending life-cycle using only the time-series of new cases per day.

  1. Dr. Praveen Gupta
  2. Prof. K.K. Sharma
  3. Prof. S.D. Joshi
  4. Dr. S. Goyal

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Abstract

The novel Coronavirus-19 disease (COVID-19) has emerged as a pandemic and has presented itself as an unprecedented challenge to the majority of countries worldwide. The containment measures for this disease such as the requirement of health care facilities greatly rely on estimating the future dynamics and flattening of the COVID-19 curve. However, it is always challenging to estimate the future trends and flattening of the COVID-19 curve due to the involvement of many real-life variables. Recently, traditional methods based on SIR and SEIR have been presented for predictive monitoring and detection of flattening of the COVID-19 curve. In this paper, a novel method for detection of flattening of the COVID-19 curve and its ending life-cycle using only the time-series of new cases per day is presented. Simulation results are compared to the SIR based methods in three different scenarios using COVID-19 curves for South Korea, the United States of America, and India. In this study, simulations, performed on the 26th April 2020 show that the peak of the COVID-19 curve in the USA has already arrived and situated on the 14th of April 2020, while the peak of the COVID-19 curve for India has yet to arrive.

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    SciScore for 10.1101/2020.05.15.20103374: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    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 10.1101/2020.05.15.20103374v1 on medRxiv