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

The CIVIC Project: A Sustainable Platform for COVID-19 syndromic-surveillance via Health, Deprivation and Mass Loyalty-Card Datasets
CIVIC 项目:通过健康、贫困和大众忠诚卡数据集进行 COVID-19 症状监测的可持续平台
批准号:
EP/V053922/1
负责人:
James Goulding
金额:
$29.81万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --

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中文摘要
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英文摘要
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.
期刊论文(10)
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会议论文
DOI: 10.7554/elife.80428
发表时间: 2023-01-24
期刊: eLife
影响因子: 7.7
作者: [Cheetham NJ, Kibble M, Wong A, Silverwood RJ, Knuppel A, Williams DM, Hamilton OKL, Lee PH, Bridger Staatz C, Di Gessa G, Zhu J, Katikireddi SV, Ploubidis GB, Thompson EJ, Bowyer RCE, Zhang X, Abbasian G, Garcia MP, Hart D, Seow J, Graham C, Kouphou N, Acors S, Malim MH, Mitchell RE, Northstone K, Major-Smith D, Matthews S, Breeze T, Crawford M, Molloy L, Kwong ASF, Doores K, Chaturvedi N, Duncan EL, Timpson NJ, Steves CJ]
通讯作者: Steves CJ
Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models
评估将全国纵向购物数据整合到呼吸道疾病预测模型中的价值
DOI: 10.21203/rs.3.rs-2226531/v1
发表时间: 2022
期刊:
影响因子: --
作者: [Dolan E]
通讯作者: Dolan E
Additional file 2 of Public attitudes towards sharing loyalty card data for academic health research: a qualitative study
附加文件 2:公众对学术健康研究共享会员卡数据的态度:定性研究
DOI: 10.6084/m9.figshare.20025010
发表时间: 2022
期刊:
影响因子: --
作者: [Dolan E]
通讯作者: Dolan E
Additional file 1 of Public attitudes towards sharing loyalty card data for academic health research: a qualitative study
附加文件 1:公众对学术健康研究共享会员卡数据的态度:定性研究
DOI: 10.6084/m9.figshare.20025007
发表时间: 2022
期刊:
影响因子: --
作者: [Dolan E]
通讯作者: Dolan E
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