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Clinicians and GPs will soon be able to better identify patients who are at a higher risk of serious illness from SARS-CoV-2 infection based on a new data-driven risk prediction model, now under development by an Oxford University-led team.

Developed in collaboration with a number of partner universities and NHS Digital, this new model could be applied in a variety of health and care settings, including supporting GPs and specialists in consultations with their patients to provide more targeted advice based on individual levels of risk. 

The model could also be used to inform mathematical modelling of the potential impact of national public health policies on shielding and preventing infection and potentially help identify those at highest risk to be vaccinated, when available.  

Principal Investigator, Professor Julia Hippisley-Cox, Professor of Epidemiology and General Practice at the University of Oxford’s Nuffield Department of Primary Care Health Sciences said:

‘Driven by real patient data, this risk assessment tool could enable a more sophisticated approach to identifying and managing those most at risk of infection and more serious COVID-19 disease.’

‘Importantly, it will provide better information for GPs to identify and verify individuals in the community who, in consultation with their doctor, may take steps to reduce their risk, or may be advised to shield.’ 

The full story is available on the University of Oxford website

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