Regression Models for Predictions of COVID-19 New Cases and New Deaths Based on May/June Data in Ethiopia

  1. Alemayehu Siffir Argawu

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Abstract

As the 15 of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 total deaths. Ethiopia was ranked 2 nd and 15 th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths based on May/June data in Ethiopia using regression model. In this study, I used Pearson’s correlation analysis and the linear regression model to predict COVID-19 new cases and new deaths based on the available data from 12 th May to 10 th June 2020 in Ethiopia. There was a significant positive correlation between COVID-19 new cases and new deaths with different related variables. In the regression models, the simple linear regression model was a better fit the data of COVID-19 new cases and new deaths than as compared with quadratic and cubic regression models. In the multiple linear regression model, variables such as the number of days, the number of new laboratory tests, and the number of new cases from AA city significantly predicted the COVID-19 new cases. In this model, the number of days and new recoveries significantly predicted new deaths of COVID-19. The number of days, daily laboratory tests, and new cases from Addis Ababa city significantly predicted new COVID-19 cases, and the number of days and new recoveries significantly predicted new deaths from COVID-19. According to this analysis, if strong preventions and action are not taken in the country, the predicted values of COVID-19 new cases and new deaths will be 590 and 12 after two months (after 9 th of August) from now, respectively. The researcher recommended that the Ethiopia government, Ministry of Health and Addis Ababa city administrative should give more awareness and protections for societies, and they should also open more COVID-19 laboratory testing centers. Generally, the obtained results of this study may help Ethiopian decision-makers put short-term future plans to face this epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.04.20188094: (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 variableThe data included the total number of new cases, date of recorded, number of new total COVID-19 cases, number of new deaths, number of new recoveries, persons who have contacted infected cases, number of male total COVID-19 cases, number of new cases from AA city, and others.

    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:
    Limitations of The study: The main limitation of this analysis was that the data were not found together as collectively for all the previous reports and were taken from the face book and telegram pages of Ethiopia Ministry of Health. Second, limiting my analysis was that some data values were missed to report for 8 dates (such as the contact and travel history of the cases). Strength of the Study: Despite all the limitations, the greatest strength of this study was the very high adjusted R2 found in the predictive model. Three predictors for COVID-19 new cases were found in the multiple linear regression model, and its assumptions were fitted. In addition, there was cross-validation with two different software programs (R and SPSS).

    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 10.1101/2020.09.04.20188094v1 on medRxiv
  3. Version published to 10.21203/rs.3.rs-61667/v1 on Research Square
  4. Version published to 10.11648/j.ijtam.20200605.11