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A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19
Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for m...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978951/ https://www.ncbi.nlm.nih.gov/pubmed/35378754 http://dx.doi.org/10.21203/rs.3.rs-1490524/v1 |
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author | Dunn, Jessilyn Shandhi, Mobashir Hasan Cho, Peter Roghanizad, Ali Singh, Karnika Wang, Will Enache, Oana Stern, Amanda Sbahi, Rami Tatar, Bilge Fiscus, Sean Khoo, Qi Xuan Kuo, Yvonne Lu, Xiao Hsieh, Joseph Kalodzitsa, Alena Bahmani, Amir Alavi, Arash Ray, Utsab Snyder, Michael Ginsburg, Geoffrey Pasquale, Dana Woods, Christopher Shaw, Ryan |
author_facet | Dunn, Jessilyn Shandhi, Mobashir Hasan Cho, Peter Roghanizad, Ali Singh, Karnika Wang, Will Enache, Oana Stern, Amanda Sbahi, Rami Tatar, Bilge Fiscus, Sean Khoo, Qi Xuan Kuo, Yvonne Lu, Xiao Hsieh, Joseph Kalodzitsa, Alena Bahmani, Amir Alavi, Arash Ray, Utsab Snyder, Michael Ginsburg, Geoffrey Pasquale, Dana Woods, Christopher Shaw, Ryan |
author_sort | Dunn, Jessilyn |
collection | PubMed |
description | Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing towards individuals who are most likely to be infected and, thus, increasing testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6,765 participants) and the MyPHD study (8,580 participants), including smartwatch data from 1,265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate features distinguished between COVID-19 positive and negative cases earlier in the course of the infection than steps features, as early as ten and five days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model’s precision-recall curve (AUC-PR) by 38–50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 3-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve allocation of diagnostic testing resources and reduce the burden of test shortages. |
format | Online Article Text |
id | pubmed-8978951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-89789512022-04-05 A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 Dunn, Jessilyn Shandhi, Mobashir Hasan Cho, Peter Roghanizad, Ali Singh, Karnika Wang, Will Enache, Oana Stern, Amanda Sbahi, Rami Tatar, Bilge Fiscus, Sean Khoo, Qi Xuan Kuo, Yvonne Lu, Xiao Hsieh, Joseph Kalodzitsa, Alena Bahmani, Amir Alavi, Arash Ray, Utsab Snyder, Michael Ginsburg, Geoffrey Pasquale, Dana Woods, Christopher Shaw, Ryan Res Sq Article Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing towards individuals who are most likely to be infected and, thus, increasing testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6,765 participants) and the MyPHD study (8,580 participants), including smartwatch data from 1,265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate features distinguished between COVID-19 positive and negative cases earlier in the course of the infection than steps features, as early as ten and five days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model’s precision-recall curve (AUC-PR) by 38–50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 3-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve allocation of diagnostic testing resources and reduce the burden of test shortages. American Journal Experts 2022-04-01 /pmc/articles/PMC8978951/ /pubmed/35378754 http://dx.doi.org/10.21203/rs.3.rs-1490524/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Dunn, Jessilyn Shandhi, Mobashir Hasan Cho, Peter Roghanizad, Ali Singh, Karnika Wang, Will Enache, Oana Stern, Amanda Sbahi, Rami Tatar, Bilge Fiscus, Sean Khoo, Qi Xuan Kuo, Yvonne Lu, Xiao Hsieh, Joseph Kalodzitsa, Alena Bahmani, Amir Alavi, Arash Ray, Utsab Snyder, Michael Ginsburg, Geoffrey Pasquale, Dana Woods, Christopher Shaw, Ryan A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title | A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title_full | A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title_fullStr | A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title_full_unstemmed | A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title_short | A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 |
title_sort | method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978951/ https://www.ncbi.nlm.nih.gov/pubmed/35378754 http://dx.doi.org/10.21203/rs.3.rs-1490524/v1 |
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