Forecasting the Epidemiological Impact of Coronavirus Disease (COVID-19): Pre-vaccination Era

  1. Saheed Oladele Amusat

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

During this pandemic, many studies have been published on the virology, diagnosis, prevention, and control of the novel coronavirus. However, fewer studies are currently available on the quantitative future epidemiological impacts. Therefore, the purpose of this study is to forecast the COVID-19 morbidities and associated-mortalities among the top 20 countries with the highest number of confirmed COVID-19 cases globally prior to vaccination intervention.

Method

We conducted a secondary data analysis of the prospective geographic distribution of COVID-19 cases data worldwide as of 10 April 2020. The historical data was forecasted using Exponential-Smoothing to detect seasonality patterns and confidence intervals surrounding each predicted value in which 95 percent of the future points are expected to fall based on the forecast.

Results

The total mean forecasted cases and deaths were 99,823 and 8,801. Interestingly, the US has the highest forecasted cases, deaths, and percentage cases-deaths ratio of 45,338, 2 358, and 5.20% respectively. China has the lowest cases, deaths, and percentage cases-deaths ratio −267, −2, and 0.75% respectively. In addition, France has the highest forecasted percentage cases-deaths ratio of 26.40% with forecasted cases, and deaths of 6,246, and 1,649 respectively.

Conclusion

Our study revealed the possibility of higher COVID-19 morbidities and associated-mortalities worldwide.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.17.21253791: (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: We detected the following sentences addressing limitations in the study:
    Strengths and limitations of the study: The study employed a systematic and rigorous approach to source for the information relevant to these research objectives. This research summarizes global distributions and quantitative impacts of COVID-19 on global health. This will stimulate the research community, stakeholders in the government and non-governmental organizations, and healthcare professionals to optimize strategies to mitigate the impact of the ongoing pandemic and strengthening preparedness for any future outbreaks. Also, it will inform the entire public to emulate compliance towards personal protective measures against the future outbreak. Our study only focuses on the top 20 countries with the highest confirmed cases in the COVID-19 global timeline. Although, it failed to reflect the entire COVID-19 cases worldwide due to redundancy in some countries’ figures.

    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

    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.

  2. Version published to 10.1101/2021.03.17.21253791 on medRxiv