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

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.

集中人群多模无线冠状病毒监测及感染预警 摘要：柯萨奇病毒气雾化传播率高，无症状潜伏期长， COVID大流行的高度可变的呈现属性已被证明具有挑战性 许多情况下，拼凑而成的大流行应对措施产生了不成比例的负面影响 受影响的弱势社会经济群体、少数民族和残疾人群体。不幸的是，这些 可怕的趋势只会在人口流动性有限和 很少或根本没有自我隔离的能力(密集的集中种群[DCP])，如住宅 养老院、学校、戒毒服务、监狱和精神病院人口， 和高频基本医疗服务，如化疗输液诊所或透析 单位。在这些DCP设置中，有限的诊断测试、长时间的室内接触、 清洁和过滤能力、支持人员短缺、先前存在的共病以及缺乏 有效的传染病监测系统都串通起来，增加了COVID的负担 在DCPS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COVID疫情的能力，减少其传播，从而减少 在这些资源不足且往往过度拥挤的地区，不公平的疾病和死亡负担 设置。在以前的工作中，我们开发了使用心率数据的第一代检测系统 从商用的Fitbit离子可穿戴设备中检测COVID和其他 在用户自我报告症状出现前10天内感染传染病(总体敏感性 67%在症状出现之前)。在这里，我们建议对该系统进行进一步的开发，以改进 使用现有可穿戴健身设备检测DCPS中的COVID和其他传染病 在集中了所有可穿戴式派生数据的无线和可互操作数字健康框架中 博士学位，同时根据IT能力和健康事件警报系统 每个DCP设置的需求。在这种情况下，我们不仅将使我们现有的感染检测 针对每个DCP的特定基线特征、IT基础架构和需求的算法，但是 还可以使用传入数据来进一步优化这些算法的性能 改善COVID发病的敏感度、特异度和警报提前时间。这将很快 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 识别个别感染事件、实施隔离、清洁和测试的数据 策略，并最大限度地减少传播，从而减轻DCP设置和 总体上降低DCP发病率和死亡率。

项目成果

Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.

• DOI: 10.1093/jamiaopen/ooac080
• 发表时间: 2022-12
• 期刊: JAMIA OPEN
• 影响因子: 2.1
• 作者: Rangan, Ekanath Srihari;Pathinarupothi, Rahul Krishnan;Anand, Kanwaljeet J. S.;Snyder, Michael P.
• 通讯作者: Snyder, Michael P.