Application of COVID-19 pneumonia diffusion data to predict epidemic situation

  1. Wu zhenguo

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Abstract

Objective

To evaluate novel coronavirus pneumonia cases by establishing the mathematical model of the number of confirmed cases daily, and to assess the current situation and development of the epidemic situation, so as to provide a digital basis for decision-making.

Methods

The number of newly confirmed covid-19 cases per day was taken as the research object, and the seven-day average value ( M ) and the sequential value ( R ) of M were calculated to study the occurrence and development of covid-19 epidemic through the analysis of charts and data.

Results

M reflected the current situation of epidemic development; R reflected the current level of infection and the trend of epidemic development.

Conclusion

The current data can be used to evaluate the number of people who have been infected, and when R < 1, the peak of epidemic can be predicted.

Preface

In December 2019, a number of cases of pneumonia with unknown causes were found in some hospitals in Wuhan, Hubei province, China. On 11 March 2020, the director-general of the world health organization (WHO), Tedros Adhanom Ghebreyesus, announced that based on the assessment, WHO believes that the current outbreak of COVID-19 can be called a global pandemic. By early April 2020, there were more than one million confirmed cases worldwide.

COVID-19 has developed from sporadic cases to pandemic in a short period of 3 months. The analysis and research of its infectious data will help to prevent and control the next stage of epidemic prevention and other infectious diseases in the future.

In this paper, COVID-19 rounded average of seven days ( M ), and M’s ring ratio ( R ) are used to predict the current potential patients’ data, and the relative state of epidemic prevention and control is judged through the graphic features and characteristic data, so as to provide evidence for the prevention and control decisions.

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    SciScore for 10.1101/2020.04.11.20061432: (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.

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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.11.20061432v1 on medRxiv