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

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          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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          Author and article information

          UCL Open: Environment Preprint
          UCL Press
          20 July 2020
          [1 ] 1. AI4Good, Kuala Lumpur, Malaysia 2. Artificial Intelligence for Medical Epidemiology (AIME), Kuala Lumpur, Malaysia
          [2 ] N/A
          [3 ] Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia
          [4 ] 4. Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, London, UK 5. Aceso Global Health Consultants Limited, London, UK

          This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

          Selangor State Government MB-SEL. 100-9/2/42 (04)

          The data that support the findings of this study are available from Helmi Zakariah but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Helmi Zakariah.

          General environmental science, Environmental engineering, Infectious disease & Microbiology, Public health

          COVID-19, Risk of Contagion, Machine Learning, Risk Ranking Area, Georeference, Environmental modelling, Sustainable development, Sustainability, Sanitation, health, and the environment, Sustainable and resilient cities


          Date: 21/7/2020

          Handling Editor: Dan Osborn

          This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment Preprint for open peer review.

          2020-09-17 13:29 UTC
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