Dissertation

Deploying Wearable Sensors for Pandemic Mitigation

Duarte, Nathan

2022

Overview

Wearable sensors can detect potential respiratory infections before or absent symptoms through continuous, passive monitoring of pathogen-elicited physiological changes. While numerous efforts have been made to develop wearable sensor-based infection detection algorithms, the population-level impact of deploying such technology during a pandemic has not been explored. In this thesis, we used mathematical modelling to study wearable sensor- based pandemic mitigation strategies. Using SARS-CoV-2 as an illustrative example, we constructed a compartmental model of Canada’s second COVID-19 wave, simulated counterfactual wearable sensor deployment scenarios, and systematically investigated the role of detection algorithm accuracy, uptake, and adherence. With currently available 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 minimised 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 pandemics. In the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.

• Publisher: ProQuest Dissertations & Theses
• Subject: Epidemiology / Infectious diseases / Pandemics / Pharmaceutical sciences / Pharmaceuticals
• ISBN: 9798380705080