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

多模式无线新冠肺炎监测

• 批准号: 10594946
• 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/10594946
• 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; Crowding; 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; Epidemiology; Event; Filtration; Frequencies; Generations; Health; Heart Rate; Incubated; Individual; Inequity; Infection; Informed Consent; Infrastructure; Infusion procedures; Institutional Review Boards; Lead; 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; Services; Severe Acute Respiratory Syndrome; Signal Transduction; Site; Societies; Specificity; Structure; Surveillance Methods; Symptoms; System; Systems Development; Testing; Time; Viral; 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 response; rehabilitation service; remote health care; smartphone application; socioeconomics; stem; surveillance data; surveillance study; transmission process; trend; usability; wearable device; wearable sensor technology; wireless; 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; Crowding; 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; Epidemiology; Event; Filtration; Frequencies; Generations; Health; Heart Rate; Incubated; Individual; Inequity; Infection; Informed Consent; Infrastructure; Infusion procedures; Institutional Review Boards; Lead; 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; Services; Severe Acute Respiratory Syndrome; Signal Transduction; Site; Societies; Specificity; Structure; Surveillance Methods; Symptoms; System; Systems Development; Testing; Time; Viral; 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 response; rehabilitation service; remote health care; smartphone application; socioeconomics; stem; surveillance data; surveillance study; transmission process; trend; usability; 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.

多模式的无线共互联监测和感染警报 摘要：共证，长期不对称孵育期的高气动传播， Covid大流行的高度可变呈现属性已被证明挑战 拼凑大流行反应的许多设置造成不成比例的负面状态 受影响的脆弱的社会经济，少数群体和残疾的亚人群。不幸的是，这些 可怕的趋势仅在以有限的流动性和有限和 几乎没有自我分离的能力（浓缩人群[DCP]），例如居民 护士房屋，学校，毒品康复服务，监狱和精神病学人口， 和高频基本医疗服务，例如化学疗法输液诊所或透析 单位。在这些DCP设置中，有限的诊断测试，延长室内接触，限制 清洁和过滤能力，支持员工短缺，预先存在合并症以及缺乏 有效的传染病监视系统都碰撞以促进增强的共同负担 在DCP中。由此，很明显，迫切需要针对DCP的替代检测策略 为了提高当地监测卷疫苗的能力，减轻其差异，从而减少 这些资源不足且经常拥挤的疾病和死亡率伯伦斯 设置。在以前的工作中，我们使用心率数据开发了第一代检测系统 从商业上可用的fitbit离子可穿戴设备来检测COVID和其他 在用户自我报告的符号发作之前，传染病长达10天（总体敏感性 症状发作之前67％）。在这里，我们建议进一步开发该系统以进行改进 使用现有的可穿戴健身设备检测DCP中的Covid和其他传染病 在无线和可互操作的数字健康框架中，该框架集中了所有可穿戴的数据 在剪裁其演示文稿和健康事件警报系统的同时，在IT功能和健康事件时 每个DCP设置的需求。在此中，我们不仅会调整现有的感染检测 每个DCP特定基线特征，IT基础架构和需求的算法，但 还使用传入数据进一步优化这些算法的性能 提高敏感性，特异性和警报提前时间的共同发作时间。这将很快 使资源不足的DCP支持人员访问和使用世界一流的Covid监视 识别单个感染事件，实施隔离，清洁和测试的数据 政策，并最小化传播，从而减少了DCP设置中的covid的伯宁 总体上降低了DCP的发病率和死亡率。