Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated Populations

多模式无线新冠肺炎监测

• 批准号: 10320756
• 负责人: MICHAEL P. SNYDER
• 金额: $ 110.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-21 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320756
• 关键词: Acute; Algorithms; Architecture; COVID detection; COVID diagnostic; COVID test; COVID testing; COVID-19 pandemic; Caring; Cellular Phone; Characteristics; Client; Clinic; Clinical; Clinical Data; Communicable Diseases; Complex; Data; Data Analyses; Data Set; Detection; Development; Devices; Diagnostic Sensitivity; Diagnostic tests; Dialysis procedure; Disabled Persons; Disease; Disease Outbreaks; Disease Surveillance; Early Diagnosis; Effectiveness; Engineering; Enrollment; Ensure; Epidemiology; Event; Filtration; Frequencies; Generations; Health; Heart Rate; Individual; Infection; Informed Consent; Infrastructure; Infusion procedures; Institutional Review Boards; Lead; Logistics; Machine Learning; Medical; Methods; Minority; Modeling; Monitor; Morbidity - disease rate; Nature; Nursing Homes; Outcome; Pathologic; Patient Recruitments; Patient Self-Report; Patients; Performance; Persons; Policies; Population; Prisons; Privacy; Process; Recommendation; Recording of previous events; Rehabilitation therapy; Reporting; Residential Facilities; Resources; Risk; Schools; Secure; Security; Sensitivity and Specificity; Services; Severe Acute Respiratory Syndrome; Signal Transduction; Site; Societies; Structure; Surveillance Methods; Symptoms; System; Systems Development; Testing; Time; Viral; Weight; Work; aerosolized; base; chemotherapy; comorbidity; coronavirus disease; dashboard; data acquisition; demographics; design; detection platform; digital health; drug rehabilitation; fitbit; fitness; higher education; improved; interoperability; machine learning algorithm; meetings; mortality; multimodality; operation; pandemic disease; rehabilitation service; remote health care; response; smartphone Application; socioeconomics; stem; surveillance data; surveillance study; transmission process; trend; wearable device; wearable sensor technology; wireless

Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated Populations Abstract: The high aerosolized transmissibility of COVID, long asymptomatic incubation period, and highly variable presentation attributes of the COVID pandemic have proven challenging in many settings where patchwork pandemic responses have disproportionately negatively impacted vulnerable socioeconomic, minority, and disabled sub-populations. Unfortunately, these dire trends are only made more acute in settings that feature populations with limited mobility and little to no ability to self-isolate (dense concentrated populations [DCPs]), such as residential nursing homes, schools, drug rehabilitation services, prison and psychiatric facility populations, and high-frequency essential medical services, such as chemotherapy infusion clinics or dialysis units. In these DCP settings, limited diagnostic testing, prolonged indoor contact, limitations in cleaning and filtration capacities, support staff shortages, pre-existing comorbidities, and lack of effective infectious disease surveillance systems all collude to drive an increased COVID burden in DCPs. From this, it is clear that alternative detection strategies for DCPs are urgently needed to improve local capacity to monitor COVID outbreaks, mitigate their spread, and thus reduce inequitable disease and mortality burdens in these under-resourced and often overcrowded settings. In previous work, we developed a first generation detection system using heart rate data from commercially-available Fitbit Ionic wearable devices to detect the onset of COVID and other infectious diseases up to 10 days before users self-reported symptom onset (overall sensitivity 67% prior to symptom onset). Here, we propose to further develop this system for the improved detection of COVID and other infectious diseases in DCPs using existing wearable fitness devices in a wireless and interoperable digital health framework that centralizes all wearable-derived data on PHD while tailoring its presentation and health event alert system to the IT capabilities and needs of each DCP setting. In this, not only will we adapt our existing infection detection algorithms for each DCP’s particular baseline characteristics, IT infrastructure, and needs, but also use incoming data to further optimize the performance of those algorithms for continuous improvement in the sensitivity, specificity, and alert lead time for COVID onset. This will quickly enable under-resourced DCP support staff to access and use world-class COVID surveillance data in identifying individual infection events, implementing isolation, cleaning, and testing policies, and minimizing transmission, thus reducing the burden of COVID in DCP settings and reducing DCP morbidity and mortality overall.

针对集中人群的多模式无线新冠肺炎监测和感染警报 摘要：新冠肺炎气溶胶传播能力强、无症状潜伏期长、 事实证明，新冠病毒大流行的高度可变的表现特征具有挑战性 在许多情况下，拼凑的流行病应对措施产生了不成比例的负面影响 影响了社会经济弱势群体、少数群体和残疾人群体。不幸的是，这些 在人口流动性有限和 几乎没有能力自我隔离（人口密集 [DCP]），例如住宅区 疗养院、学校、戒毒康复服务机构、监狱和精神病院的人群， 以及高频基本医疗服务，例如化疗输液诊所或透析 单位。在这些 DCP 环境中，有限的诊断测试、长时间的室内接触、限制 清洁和过滤能力、支持人员短缺、已有的合并症以及缺乏 有效的传染病监测系统共同导致新冠肺炎负担增加 在 DCP 中。由此可见，迫切需要针对 DCP 的替代检测策略 提高当地监测新冠疫情爆发、减缓其传播的能力，从而减少 在资源不足且经常过度拥挤的地区，疾病和死亡负担不公平 设置。在之前的工作中，我们开发了第一代使用心率数据的检测系统 从市售的 Fitbit Ionic 可穿戴设备检测新冠肺炎和其他疾病的发作 用户自我报告症状出现前 10 天的传染病（总体敏感性 67% 出现症状前）。在此，我们建议进一步开发该系统，以提高 使用现有的可穿戴健身设备检测 DCP 中的新冠肺炎和其他传染病 在无线和可互操作的数字健康框架中，集中所有可穿戴设备衍生的数据 在 PHD 上，同时根据 IT 能力定制其演示和健康事件警报系统 每个 DCP 设置的需要。在这方面，我们不仅会调整现有的感染检测 针对每个 DCP 的特定基线特征、IT 基础设施和需求的算法，但是 还使用传入数据进一步优化这些算法的性能，以实现连续 提高新冠肺炎发病的敏感性、特异性和警报提前时间。这会很快 使资源不足的 DCP 支持人员能够访问和使用世界一流的 COVID 监控 识别个体感染事件、实施隔离、清洁和测试的数据 政策，并最大限度地减少传播，从而减轻 DCP 环境中的新冠病毒负担 总体降低 DCP 发病率和死亡率。