Exposure Notification System activity as a leading indicator for SARS-COV-2 caseload forecasting

Abstract

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. 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.

Using Bayesian inference, we found that 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.