A0306
Title: A functional regression for predicting COVID-19 case counts from wastewater surveillance
Authors: Xiaotian Dai - Illinois State University (United States) [presenting]
Abstract: Wastewater-based surveillance (WBS) has become a critical tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic. However, recorded viral copy numbers in municipal wastewater can include measurement errors and missing values due to irregular or sparse sampling. A Bayesian functional regression framework is presented for predicting daily positive cases based on irregular wastewater observations, utilizing advanced statistical techniques. The proposed framework has two main goals: 1) Estimating a smooth and continuous time-based function from the irregularly observed and noisy SARS-CoV-2 RNA wastewater data, and 2) combining this smooth function with recorded COVID-19 cases to demonstrate the potential for predicting future positive cases from SARS-CoV-2 RNA in wastewater. The proposed framework is adaptable to varying vaccination rates, weather patterns, seasonal factors in different jurisdictions, and, importantly, the participation bias in COVID-19 testing, making it applicable to other WBS studies.
