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Updated: Jul 19, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

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Published on: June 23, 2022

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

Eliah Aronoff-Spencer¹, Sepideh Mazrouee¹, Rishi Graham¹

¹School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America.

|August 18, 2023

Summary

Digital Exposure Notification (EN) systems can improve COVID-19 case forecasting. Integrating EN data into models significantly enhanced prediction accuracy, offering potential for early warning systems.

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Area of Science:

Epidemiology
Public Health Informatics
Computational Biology

Background:

COVID-19 pandemic spurred development of digital Exposure Notification (EN) systems.
These systems offer novel avenues for public health interventions.
Limited research exists on utilizing real-time EN data for predictive epidemiological modeling.

Purpose of the Study:

To assess the utility of real-time data from California's CA Notify (Google Apple Exposure Notification - GAEN platform) for short-term COVID-19 case forecasting.
To determine if incorporating EN activity improves predictive accuracy compared to traditional models.

Main Methods:

Extended a statistical model using historical case counts to predict future caseloads.
Integrated anonymized EN activity data from CA Notify into the forecasting model.
Compared model performance (with and without EN data) against actual reported caseloads for 1-7 day forecasts.

Main Results:

Time series analysis revealed a temporal association between EN system activity and COVID-19 caseloads.
Incorporating EN data significantly improved short-term caseload prediction accuracy.
Bayesian inference confirmed a non-zero influence of EN terms, reducing Mean Absolute Percentage Error and Mean Squared Prediction Error by 5-32%.

Conclusions:

Smartphone-based EN systems demonstrably enhance the accuracy of short-term epidemiological forecasts.
These predictive models show promise for deployment as local early warning systems for resource allocation and intervention planning.