Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada's second COVID-19 wave.

ABSTRACT: Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada's second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasin

SUBMITTER: Duarte N 

PROVIDER: S-EPMC9931244 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature