A statistical forecast of LOW mortality and morbidity due to COVID-19, in ARGENTINA and other Southern Hemisphere countries

  1. Cesar A. Barbero

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Abstract

A set of open source programs in Python is devised to fit a parametric integrated Gaussian equation to cumulative deaths due to COVID-19 in Southern Hemisphere countries. The programs were successfully tested using data from advanced outbreak trajectories (Italy and Spain). The procedure was applied to data reported by Argentina. The projected total death toll will be 182 (277-182) with a peak of deaths (6(+/-2)) the 14 of April. The outbreak begins the 9 th of March and end completely the 20 th of May. However, already on 1 st of May, 2 s (95 . 45%) of the deaths have occurred. The death toll arises from a number of infected individuals between 36412 and 2275 . Then, they were to use to process data from several Southern Hemisphere countries: Argentina, Brazil, Mexico, Peru, Colombia, Ecuador, Cuba, Chile, Panama, Australia, Bolivia, Honduras, New Zealand, Paraguay, Guatemala, Venezuela, Uruguay, El Salvador, Jamaica, Haiti, Costa Rica and Nicaragua. The trend is to show low number of total deaths compared with other disease outbreaks. A total projected number of deaths between 15148 and 9939 deaths for a total population of ca. 664 M inhabitants. The projected death toll is much lower (5-10 times) than those forecasted by the Imperial College Group (ICG) even considering the best scenario of total suppression of virus transmission. Using actual mortality rates it is possible to back calculate which number of infected individuals would produce such mortality. The calculated number of infected individuals (worst case scenario) is below 2.5 million. This is significantly lower than that calculated by ICG (> 45 millions). In most countries the outbreak will end in May or early June. The dynamics of the outbreaks seems to do not saturate the health services (hospital beds) but only Peru, Ecuador and Panama should have not enough ICU beds for grave COVID-19 patients.

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    SciScore for 10.1101/2020.04.20.20072488: (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: Thank you for sharing your code and data.

    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:
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    • No protocol registration statement was detected.

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  2. ScreenIT

    SciScore for 10.1101/2020.04.20.20072488: (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

    Software and Algorithms
    SentencesResources
    Experimental The program was developed in Python 3.76 (https://www.google.com/search?client=firefox-bd&q=python) in the IDE Spyder (https://www.spyder-ide.org/) launched by Anaconda (https://www.google.com/search?client=firefox-b-d&q=anaconda+python) on a Dell Latitude 3460 laptop (i3, 4 Gb of memory and 600 Gb of disk).
    Python
    suggested: (IPython, SCR_001658)
          <div style="margin-bottom:8px">
        <div><b>https://www.spyder-ide.org/</b></div>
        <div>suggested: (Spyder , <a href="https://scicrunch.org/resources/Any/search?q=SCR_017585">SCR_017585</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Method: fit with routines in Python (scipy.optimize, https://www.scipy.org/) in our own Python programs (available at https://github.com/cesarbarbero/programas-para-predecir-COVID-19/).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>scipy</b></div>
        <div>suggested: (SciPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008058">SCR_008058</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The author appreciates the use of open source programs (Python, Anaconda, Spyder, Matplotlib, Scipy, Numpy, Euler Math Toolbox, Vseuz graphics software).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Matplotlib</b></div>
        <div>suggested: (MatPlotLib, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008624">SCR_008624</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>Numpy</b></div>
        <div>suggested: (NumPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008633">SCR_008633</a>)</div>
      </div>
    </td></tr></table>

    Results from OddPub: Thank you for sharing your code.

    About SciScore

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  3. Version published to 10.1101/2020.04.20.20072488v1 on medRxiv