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, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable. Author summary Find-Test-Trace-Isolate (FTTI) systems reliant on lab-based tests are important components of pandemic mitigation but can miss infectious individuals that do not have symptoms and may be limited by slow test result turnaround times. Wearable sensors show promise in continuous, passive detection of respiratory infections, before or absent symptoms. Here, we used a mathematical model to study the counterfactual impact of deploying wearable sensors to detect SARS-CoV-2 infections during Canada’s second COVID-19 wave. We observed a meaningful reduction in the burden of infection but also found that false positive alerts resulting from imperfect detection specificity resulted in high social and resource costs. Improving detection specificity and offering rapid antigen tests to confirm positive alerts both helped minimize unnecessary quarantines and lab-based tests. We found that once the false positive rate was sufficiently reduced, increasing uptake and adherence became effective strategies to scale the number of averted infections. Our study demonstrates that wearable sensors capable of detecting infections before or absent symptoms are promising pandemic mitigation tools. It also provides intuition around how detection performance, uptake, adherence, and supporting policies might shape the impact of broad scale wearable sensor deployment.