Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19

Bryan Conroy,Ting Feng,Anne G Rizzo,Niels Olson,Golbarg Mehraei,Robert Damiano,Brian Gross,Emmanuele Salvati,Joseph Frassica,Ikaro Silva,Catherine M Curtin,Jeffrey Schneider,Daniel C Mcfarlane

doi:10.1038/s41598-022-07764-6

Bryan Conroy, Ting Feng + Show 11 more

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https://doi.org/10.1038/s41598-022-07764-6

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Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Highlights

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce
The goal of this effort was to rapidly operationalize the Rapid Analysis of Threat Exposure (RATE) algorithm[32], which was originally developed by Philips under a Defense Threat Reduction Agency (DTRA) and Defense Innovation Unit (DIU)-sponsored program (2018–2019) to predict hospital-acquired infection
It was hypothesized that RATE could deliver a pre-symptomatic early warning service for exposure to SARS-CoV-2 to support and maintain mission readiness of healthcare personnel and critical Department of Defense (DoD) staff during the COVID-19 pandemic

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Introduction

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. The present article reports on US DoD-sponsored research to investigate use of wearable physiological monitoring for early prediction of infection and real-time notification of potential exposures via predictive machine learning.

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