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

鉴于正在发生的COVID-19感染以及即将到来的第二波感染，迫切需要：大大提高了对全英国未记录病例的估计。N2。在全英国范围内的大量行为数据中确定COVID的关键前兆，这些数据可以为急需的大规模预警系统提供动力；可持续;而且不依赖自我报告应用程序。N3。模拟对隐藏的弱势社区（如粮食贫困、ame）的影响，以帮助制定长期干预战略。CIVIC的理想定位是通过无与伦比的海量行为数据访问粒度来满足这些需求；独特的伙伴关系：私营部门数据提供商（如Boots、OLIO、Fareshare）、学术专家（流行病学、行为科学、人工智能/统计学）和公共部门影响力合作伙伴（ONS、JBC、NHS-X）通过3个相互关联的工作包构建了一个前所未有的平台：WP1。与Boots/NHS合作，通过查询大量项目健康/药房交易数据（根据111电话数据进行验证），生成首个未经测试的COVID-19病例可持续模型。WP2。COVID-19暴发的行为和临床前因的识别通过人工智能/机器学习技术处理大量零售会员卡/销售点日志，生成近期预测，支撑预警系统。WP3。模拟对主要脆弱社区的潜在社会/经济影响，以实际行为模式而不是简单的人口预测来确定。每个WP有2个阶段。第一阶段的重点是严格匿名的汇总数据，这些数据来自150亿条交易记录，在短短4个月内为英国32,884个社区（lsoa）中的每个社区提供重要的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.