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| Funder | COVID-19 Research Funding |
|---|---|
| Recipient Organization | University of Nottingham |
| Country | United Kingdom |
| Start Date | Feb 01, 2021 |
| End Date | Jan 31, 2022 |
| Duration | 364 days |
| Number of Grantees | 8 |
| Roles | Co-Investigator; Principal Investigator; Award Holder |
| Data Source | UKRI Gateway to Research |
| Grant ID | EP/V053922/1 |
In light of ongoing COVID-19 infections, and approaching second waves, there is urgent need to: N1. Vastly improve estimation of UK-wide unrecorded cases.
N2. Identify key antecedents of COVID in mass, UK-wide behavioural data, that can power urgently needed early-warning systems at scale; sustainably; and without reliance on self-reporting apps.
N3. Model impact to hidden, vulnerable communities (e.g. food poverty, BAME), to help long-term intervention strategies.
CIVIC is ideally placed to address these needs via unparalleled granularity of access to mass behavioural data; A unique partnership: private-sector data-providers (e.g. Boots, OLIO, Fareshare), academic expertise (Epidemiology, Behavioural Science, AI/Statistics), and public-sector impact partners (ONS, JBC, NHS-X) building an unprecedented platform via 3 interlinked work-packages:
WP1. Partnership with Boots/NHS to generate first-ever, sustainable models of untested COVID-19 cases through interrogation of mass, line-item health/pharmacy transaction data (validated against 111-call-data).
WP2. Identification of behavioural and clinical antecedents of COVID-19 outbreak; processing mass retail loyalty-card/point-of-sale logs via AI/machine-learning techniques, generating near-future forecasts, underpinning early-warning systems.
WP3. Modelling of hidden social/economic impacts to key vulnerable communities, identified in actual behavioural patterns not simple demographic projections.
Each WP has 2 stages. Stage-1 focuses on strictly-anonymized, aggregated data derived from >1.5 billion transactional records, providing crucial deliverables and revolutionizing insights for each of the UK's 32,884 neighbourhoods (LSOAs) within just 4 months. Stage-2 increases fidelity, via individual-level modelling via a ground-breaking "Data Donation" framework.
Imperial College London; University of Nottingham; University of Bristol
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