The Dawn is Coming —— the Description and Prediction of Omicron SARSCoV-2 Epidemic Outbreak in Shanghai by Mathematical Modeling

  1. Minghao Jiang
  2. Guoyu Meng
  3. Geng Wu

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The COVID-19 Omicron outbreak in Shanghai has been going on for >1 month and 25 million population is subjected to strict lock-down quarantine. Until now, it is not clear how long this epidemic might end. Here, we present a time-delayed differentiation equation model to evaluate and forecast the spreading trend. Our model provides important parameters such as the average quarantine ratio, the detection interval from being infected to being tested positive, and the spreading coefficient to better understand the omicron progression. After data fitting, we concluded on 11 April that the maximum overall number infected in Shanghai would exceed 300,000 on 14 April and the turning point would be in the coming days around 13-15 April, 2022, which is perfectly in line with the real-life infection number. Furthermore, the quarantine ratio in Shanghai was found to be greater than 1, supportive of the effectiveness of the strict lockdown policy. Altogether, our mathematical model helps to define how COVID-19 epidemic progresses under the Shanghai lock-down unprecedented in human history and the Chinese zero tolerance policy.

Article activity feed

  1. Version published to 10.1101/2022.04.13.22273788v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2022.04.13.22273788: (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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.04.13.22273788v1 on medRxiv