The CIVIC Project: A Sustainable Platform for COVID-19 syndromic-surveillance via Health, Deprivation and Mass Loyalty-Card Datasets

  • Funded by UK Research and Innovation (UKRI)
  • Total publications:11 publications

Grant number: EP/V053922/1

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $315,852.75
  • Funder

    UK Research and Innovation (UKRI)
  • Principal Investigator

    James Goulding
  • Research Location

    United Kingdom
  • Lead Research Institution

    University of Nottingham
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease transmission dynamics

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Minority communities unspecifiedVulnerable populations unspecified

  • Occupations of Interest

    Not applicable

Abstract

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.

Publicationslinked via Europe PMC

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View all publications at Europe PMC

Detecting iodine deficiency risks from dietary transitions using shopping data.

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.

Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors in two UK longitudinal studies.

Inequalities in healthcare disruptions during the COVID-19 pandemic: evidence from 12 UK population-based longitudinal studies.

Association Between Purchase of Over-the-Counter Medications and Ovarian Cancer Diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study.

A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19.

Public attitudes towards sharing loyalty card data for academic health research: a qualitative study.

External validation of a model to predict women most at risk of postpartum venous thromboembolism: Maternity clot risk.

Social eating initiatives and the practices of commensality.