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

鉴于正在进行的新冠肺炎感染，以及即将到来的第二波，迫切需要：N1。极大地改善了对全英国未记录病例的估计。氮气。在英国范围内的海量行为数据中确定COVID的关键前因，这些数据可以大规模、可持续地为迫切需要的早期预警系统提供动力，并且不依赖自我报告应用程序。N3。CIVIC是通过无与伦比的获取大规模行为数据的粒度来满足这些需求的理想位置；独特的合作伙伴关系：私营部门数据提供者(例如Boots、Olio、Fareshare)、学术专业知识(流行病学、行为科学、人工智能/统计)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一个前所未有的平台：WP1。与博姿/英国国民健康保险制度合作，通过询问海量、分类的医疗/药房交易数据(根据111呼叫数据进行验证)，首次生成未经测试的新冠肺炎案例的可持续模型。WP2。识别新冠肺炎爆发的行为和临床前兆；通过人工智能/机器学习技术处理大量零售业忠诚卡/销售点日志，生成近期预测，为早期预警系统提供支持。WP3。对关键弱势社区的隐性社会/经济影响进行建模，在实际行为模式中确定，而不是简单的人口预测。每个WP有两个阶段。第一阶段专注于严格匿名的聚合数据，这些数据来自于15亿笔交易记录，在短短4个月内为英国32,884个居民区(LSO)提供了关键的交付成果并带来了革命性的见解。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.