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

鉴于持续的COVID-19感染，并接近第二波，迫切需要：N1.大大提高了对全英国未记录病例的估计。氮气在英国范围内的大规模行为数据中识别COVID的关键前因，这些数据可以大规模、可持续地为急需的预警系统提供动力，而无需依赖自我报告应用程序。N3。模型对隐藏的脆弱社区的影响CIVIC通过无与伦比的大规模行为数据访问粒度，是满足这些需求的理想选择;独特的合作伙伴关系：私营部门数据提供者（如Boots、OLIO、Fareshare）、学术专长（流行病学，行为科学，人工智能/统计学）和公共部门影响合作伙伴（ONS，JBC，NHS-X）通过3个相互关联的工作包建立了一个前所未有的平台：WP 1。与Boots/NHS合作，通过询问大量的、单项的健康/药房交易数据（根据111呼叫数据进行验证），为未经测试的COVID-19病例生成首个可持续模型。WP 2.识别COVID-19爆发的行为和临床前因;通过人工智能/机器学习技术处理大规模零售忠诚卡/销售点日志，生成近期预测，支持预警系统。WP 3.对主要脆弱社区的潜在社会/经济影响建模，在实际行为模式中识别，而不是简单的人口预测。第一阶段专注于从超过15亿笔交易记录中提取的严格匿名的聚合数据，在短短4个月内为英国32，884个社区（LSOA）中的每个社区提供关键的可交付成果和革命性的见解。第二阶段通过突破性的“数据捐赠”框架，通过个人层面的建模来提高保真度。

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.