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    Review of 'Application of Inherent Risk of Contagion (IRC) framework and modelling to aid local Covid-19 response and mitigation'

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    Application of Inherent Risk of Contagion (IRC) framework and modelling to aid local Covid-19 response and mitigationCrossref
    The idea is novel to provide the support for pandemic , but it needs more detail explaination.
    Average rating:
        Rated 3.5 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 3 of 5.
    Level of completeness:
        Rated 3 of 5.
    Level of comprehensibility:
        Rated 3 of 5.
    Competing interests:
    None

    Reviewed article

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    • Article: found
    Is Open Access

    Application of Inherent Risk of Contagion (IRC) framework and modelling to aid local Covid-19 response and mitigation

    The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 represents a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that has the potential to play a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework that, through the geo-referencing of COVID-19 cases in a particular region, is able to provide support to operational platforms from which response and mitigation activities can be planned and executed. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.
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      Review information

      10.14293/S2199-1006.1.SOR-ENG.AKF9OA.v1.RZVECS

      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.

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      Review text

      The research paper proposes a novel idea of providing the support for pandemic management by doing a geolocation tracking of the covid 19 positive cases. However a more detailed explaination about the developed AI framework and ways of integration with the pandemic management teams must be explained more clearly. Also, the validation of the developed framework must be also included.

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      2021-04-24 12:32 UTC
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      2021-04-24 12:32 UTC
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