Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models.

ABSTRACT: During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of rout

SUBMITTER: Lin T 

PROVIDER: S-EPMC10673837 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature