Review of '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.