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    • Review: found
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    Review of 'An Engineering Model of the COVID-19 Trajectory to Predict Success of Isolation Initiatives'

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    An Engineering Model of the COVID-19 Trajectory to Predict Success of Isolation InitiativesCrossref
    The MS presents a simple model that works, but some major improvements should be done to the MS
    Average rating:
        Rated 2 of 5.
    Level of importance:
        Rated 1 of 5.
    Level of validity:
        Rated 2 of 5.
    Level of completeness:
        Rated 2 of 5.
    Level of comprehensibility:
        Rated 3 of 5.
    Competing interests:
    None

    Reviewed article

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    • Abstract: found
    • Article: found
    Is Open Access

    An Engineering Model of the COVID-19 Trajectory to Predict Success of Isolation Initiatives

     Steven King,  Alberto Striolo (corresponding) (2020)
    The development of the Covid-19 pandemic both in terms of geographical footprint and the growth of cases and fatalities has been the subject of opportune comment and provided the news media with constant and compelling feed. Because the media-reported current state and expected future outcomes show wide variation, modelling has been attempted here using an engineering differential model to provide an evidence-based statement of future expectations.
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      Review information

      10.14293/S2199-1006.1.SOR-ENG.AQM0HA.v1.RDETXM

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Keywords:

      Review text

      Please follow the structure of the manuscript that is published at the Journal webpage

      Introduction is too short and with few papers. Please move to Methods all the methodology parts that nor are in Introduction.

      Please compare your model to SEIR epidemiological models (for example in Bordehore et al 2020 https://www.medrxiv.org/content/10.1101/2020.03.30.20047043v1 ) 

      or in basic epidemiological publications dealing with SEIR models and intrinsic growth rate (instead of epidemiological R) 

      Your K1 would correspond to the "intrinsic growth rate" in population dynamics literature. Please add some references.

      Please add references when necessary.

      Graphs should be redrawn in a better way, some text is overlapped and lines are too thick.

      Please remove Herd Immunity, or include the problems derived from the utterly high death toll if a community decides to reach 70-75% of immunity.

       

       

         

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