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Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting

PURPOSE: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the im...

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Autores principales: Aronoff-Spencer, Eliah, Mazrouee, Sepideh, Graham, Rishi, Handcock, Mark S., Nguyen, Kevin, Nebeker, Camille, Malekinejad, Mohsen, Longhurst, Christopher A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437830/
https://www.ncbi.nlm.nih.gov/pubmed/37594936
http://dx.doi.org/10.1371/journal.pone.0287368
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author Aronoff-Spencer, Eliah
Mazrouee, Sepideh
Graham, Rishi
Handcock, Mark S.
Nguyen, Kevin
Nebeker, Camille
Malekinejad, Mohsen
Longhurst, Christopher A.
author_facet Aronoff-Spencer, Eliah
Mazrouee, Sepideh
Graham, Rishi
Handcock, Mark S.
Nguyen, Kevin
Nebeker, Camille
Malekinejad, Mohsen
Longhurst, Christopher A.
author_sort Aronoff-Spencer, Eliah
collection PubMed
description PURPOSE: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS: We investigated the potential to short-term forecast COVID-19 caseloads using data from California’s implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident’s smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1–7 days in the future. RESULTS: Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS: This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
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spelling pubmed-104378302023-08-19 Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting Aronoff-Spencer, Eliah Mazrouee, Sepideh Graham, Rishi Handcock, Mark S. Nguyen, Kevin Nebeker, Camille Malekinejad, Mohsen Longhurst, Christopher A. PLoS One Research Article PURPOSE: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS: We investigated the potential to short-term forecast COVID-19 caseloads using data from California’s implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident’s smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1–7 days in the future. RESULTS: Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS: This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions. Public Library of Science 2023-08-18 /pmc/articles/PMC10437830/ /pubmed/37594936 http://dx.doi.org/10.1371/journal.pone.0287368 Text en © 2023 Aronoff-Spencer et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Aronoff-Spencer, Eliah
Mazrouee, Sepideh
Graham, Rishi
Handcock, Mark S.
Nguyen, Kevin
Nebeker, Camille
Malekinejad, Mohsen
Longhurst, Christopher A.
Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title_full Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title_fullStr Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title_full_unstemmed Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title_short Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting
title_sort exposure notification system activity as a leading indicator for sars-cov-2 caseload forecasting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437830/
https://www.ncbi.nlm.nih.gov/pubmed/37594936
http://dx.doi.org/10.1371/journal.pone.0287368
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