An exploration of factors characterising unusual spatial clusters of COVID-19 cases in the East Midlands region, UK: A geospatial analysis of ambulance 999 data

Complex interactions between physical landscapes and social factors increase vulnerability to emerging infectionsand their sequelae. Relative vulnerability to severe illness and/or death (VSID) depends on risk andextent of exposure to a virus and underlying health susceptibility. Identifying vulnerable communities and theregions they inhabit in real time is essential for effective rapid response to a new pandemic, such as COVID-19. Inthe period between first confirmed cases and the introduction of widespread community testing, ambulancerecords of suspected severe illness from COVID-19 could be used to identify vulnerable communities and regionsand rapidly appraise factors that may explain VSID. We analyse the spatial distribution of more than 10,000suspected severe COVID-19 cases using records of provisional diagnoses made by trained paramedics attendingmedical emergencies. We identify 13 clusters of severe illness likely related to COVID-19 occurring in the EastMidlands of the UK and present an in-depth analysis of those clusters, including urban and rural dynamics, thephysical characteristics of landscapes, and socio-economic conditions. Our findings suggest that the dynamics ofVSID vary depending on wider geographic location. Vulnerable communities and regions occur in more deprivedurban centres as well as more affluent peri-urban and rural areas. This methodology could contribute to thedevelopment of a rapid national response to support vulnerable communities during emerging pandemics in realtime to save lives.