Deploying wearable sensors for pandemic mitigation

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 found that deploying wearable sensors could have averted 9% of second wave SARS-CoV-2 infections, though 29% of this reduction is attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized incorrect quarantines and associated costs. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensor deployment can meaningfully contribute to pandemic mitigation; in the case of COVID-19, technology improvements or supporting measures are required to reduce social and economic costs to acceptable levels.