Uncovering Dynamic Relationships Between SARS-CoV-2 Wastewater Concentrations and Community Infections via Bayesian Spatial Functional Concurrent Regression

• Published in: Data science in science Vol. 4; no. 1
• Main Authors: Sun, Thomas Y., Schedler, Julia C., Kowal, Daniel R., Schneider, Rebecca, Stadler, Lauren B., Hopkins, Loren, Ensor, Katherine B.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31.12.2025
• Subjects: COVID-19 data; factor models; sparse functional data
• ISSN: 2694-1899; 2694-1899
• DOI: 10.1080/26941899.2025.2521329

Monitoring wastewater concentrations of SARS-CoV-2 yields a low-cost, noninvasive method for tracking disease prevalence and provides early warning signs of upcoming outbreaks in the serviced communities. There is tremendous clinical and public health interest in understanding the complex relationships between wastewater viral loads and infection rates in the population. As both data sources may contain substantial noise and missingness, in addition to spatial and temporal dependencies, properly modeling this relationship must address these numerous complexities simultaneously while providing interpretable and clear insights. We propose a novel Bayesian functional concurrent regression model that accounts for both spatial and temporal correlations while estimating the time-dependent effects between wastewater concentrations and positivity rates over time. We explicitly model the time lag between the two series and provide full posterior inference on the possible delay between spikes in wastewater concentrations and subsequent outbreaks. We estimate a time lag likely between 5 to 11 days between spikes in wastewater levels and reported clinical positivity rates. Additionally, we find a dynamic relationship between wastewater concentration levels and the strength of its association with positivity rates that fluctuates between outbreaks and non-outbreaks.