Author: Bowyer, Ruth; Varsavsky, Thomas; Sudre, Carole H; Murray, Benjamin; Freidin, Maxim; Yarand, Darioush; Ganesh, Sajaysurya; Capdevila, Joan; Thompson, Ellen J; Bakker, Elco; Cardoso, M Jorge; Davies, Richard; Wolf, Jonathan; Spector, Tim D; Ourselin, Sebastien; Steves, Claire J; Menni, Cristina
Title: Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app Cord-id: r6s43osu Document date: 2020_4_27
ID: r6s43osu
Snippet: Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 2,266,235 unique GB users of the COVID Symptom Tracker app, we find that COVID-19 prevalence and severity became rapidly distributed across the UK within a month of the WHO declaration of the pandemic, with significant evidence of urban hot-spots. We found a geo-social gradient associated with disease severity and prevalence
Document: Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 2,266,235 unique GB users of the COVID Symptom Tracker app, we find that COVID-19 prevalence and severity became rapidly distributed across the UK within a month of the WHO declaration of the pandemic, with significant evidence of urban hot-spots. We found a geo-social gradient associated with disease severity and prevalence suggesting resources should focus on urban areas and areas of higher deprivation. Our results demonstrate use of self-reported data to inform public health policy and resource allocation.
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