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

Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated PopulationsAbstract: The high aerosolized transmissibility of COVID, long asymptomatic incubation period,and highly variable presentation attributes of the COVID pandemic have proven challenging inmany settings where patchwork pandemic responses have disproportionately negativelyimpacted vulnerable socioeconomic, minority, and disabled sub-populations. Unfortunately, thesedire trends are only made more acute in settings that feature populations with limited mobility andlittle to no ability to self-isolate (dense concentrated populations [DCPs]), such as residentialnursing homes, schools, drug rehabilitation services, prison and psychiatric facility populations,and high-frequency essential medical services, such as chemotherapy infusion clinics or dialysisunits. In these DCP settings, limited diagnostic testing, prolonged indoor contact, limitations incleaning and filtration capacities, support staff shortages, pre-existing comorbidities, and lack ofeffective infectious disease surveillance systems all collude to drive an increased COVID burdenin DCPs. From this, it is clear that alternative detection strategies for DCPs are urgently neededto improve local capacity to monitor COVID outbreaks, mitigate their spread, and thus reduceinequitable disease and mortality burdens in these under-resourced and often overcrowdedsettings. In previous work, we developed a first generation detection system using heart rate datafrom commercially-available Fitbit Ionic wearable devices to detect the onset of COVID and otherinfectious diseases up to 10 days before users self-reported symptom onset (overall sensitivity67% prior to symptom onset). Here, we propose to further develop this system for the improveddetection of COVID and other infectious diseases in DCPs using existing wearable fitness devicesin a wireless and interoperable digital health framework that centralizes all wearable-derived dataon PHD while tailoring its presentation and health event alert system to the IT capabilities andneeds of each DCP setting. In this, not only will we adapt our existing infection detectionalgorithms for each DCP's particular baseline characteristics, IT infrastructure, and needs, butalso use incoming data to further optimize the performance of those algorithms for continuousimprovement in the sensitivity, specificity, and alert lead time for COVID onset. This will quicklyenable under-resourced DCP support staff to access and use world-class COVID surveillancedata in identifying individual infection events, implementing isolation, cleaning, and testingpolicies, and minimizing transmission, thus reducing the burden of COVID in DCP settings andreducing DCP morbidity and mortality overall.