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.

针对集中人群的多模态无线COVID监测和感染警报

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.