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

    Application of Inherent Risk of Contagion (IRC) framework and modelling to aid local Covid-19 response and mitigationCrossref
    A tool to management tool for Covid-19 at local geographic scales is outlined; but it lacks detail.
    Average rating:
        Rated 2.5 of 5.
    Level of importance:
        Rated 3 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:
    Note that as the corresponding author is linked to UCL I would not normally offer to review, but as only one other reviewer has come forward I have reviewed this article.

    Reviewed article

    • Record: found
    • Abstract: found
    • 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.

      Review information


      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


      Review text

      This paper has the potential to provide local authorities and governments with a tool to help manage outbreaks of Covid-19 that is still resurgent in many parts of the world.

      Unfortunately, the authors provide too little detail of the AI framework and insufficient validation material to permit full publication until the current draft undergoes substantial revision. In this I agree with the first reviewer's short but targeted comments.

      The following detailed comments may help the authors revision of the text:

      1. The paper is short and to the point but more detail is required throughout, including on matters such as the study rationale, and its aims and objectives, to help the reader judge the work’s overall validity and applicability to any given situation. It is impossible to tell from the text whether the approach is specific to the locality for which it seems to have been developed or is of more general applicability.
      2. The study rationale needs establishing more clearly so that the study’s aims and objectives can be understood and the reader can better appreciate how the authors see the IRC estimates being used to manage Covid-19 outbreaks or chronic and continuing rates of infection in the various communities that are the subject of study. It is clear the authors want to improve the management of the pandemic at local scales but doing this will involve far more than just using a set of IRC estimates for each locality. Perhaps the presentation of validation material from the area said to be trialing the approach would help here. This could be one of several case studies that the author’s might make use of when revising their text to account for reviewer’s remarks.
      3. Insufficient descriptive material is provided on the character of the various areas that are covered by Fig 1. This is important as attempts to understand the dynamics of human intercourse in urban areas (the environment in which most people now live) must be a part of understanding more about human ecology and, currently in particular, the management of Covid-19. A framework like that outlined in this text could play an important role in improving understanding and guiding decision-making in this pandemic. A basic piece of information, for example, would be the density of populations in the different areas and another piece of relevant information could be the level of social interaction in the different areas that result from the nature of communities in each area.
      4. The AI framework is unclear. More needs to be written about the basic design of the AI framework, what AI techniques have been employed and why an AI approach has been preferred over other approaches. It is not clear if, for example, the AI approach used is just processing large amounts of data or is actually performing some form of machine-learning that will help progressively refine the management of Covid 19 in communities of different densities and characteristics. If machine-learning is involved what form this takes and how it translates into practical action in communities needs to be explained. For example, does the tool take the dynamics of the spread of Covid-19 in communities into account or the different levels of infectivity shown by different Covid-19 variants?
      5. Greater clarity is required on the definition of the rate of contagion terminology and one would normally expect to see some form of equation or framework statement indicating what factors have contributed to the IRC estimate and whether, for example, any greater weight has been associated with particular factors. This might be the case, for example, if any form of probabilistic estimations have been made to judge the value to assign to factors that link one variable to another in those cases where no quantified causal linkage might exist between factors but where this needs to be represented in the AI coding. Likewise, the authors say they have calculated a similarity index but its components and how they have been used to construct the index are entirely unclear. Also, the relationship between the similarity index and the IRC estimate are not set out clearly enough neither is the role of the similarity index in the IRC. Indeed, as written, it could be that the similarity index and the IRC are rather similar in their import for Covid-19 management. If this is the case (or even if it is not) the reader needs the clarity that a few extra sentences could bring to the paper.
      6. The importance of providing some validation for the approach has already been stated in this review and in the other.  This is very important, so the point is stressed here. As the paper text already claims the approach is in use in one location then the relevant case study should be set out in the paper with indications as to how the approach has affected both public health and public health management during the pandemic period. If the approach is being applied elsewhere than in the area already indicated information on those cases would also be welcome so that readers can see some measure of the general applicability of this approach to the pandemic.


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