Application of joinpoint regression to SARS-CoV-2 wastewater-based epidemiology in Las Vegas, Nevada, USA.

Temporal variability and methodological differences in data normalization, among other factors, complicate effective trend analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater surveillance data and its alignment with coronavirus disease 2019 (COVID-19) clinical outcomes. As there is no consensus approach for these analyses yet, this study explored the use of piecewise linear trend analysis (joinpoint regression) to identify significant trends and trend turning points in SARS-CoV-2 RNA wastewater concentrations (normalized and non-normalized) and corresponding COVID-19 case rates in the greater Las Vegas metropolitan area (Nevada, USA) from mid-2020 to April 2023. The analysis period was stratified into three distinct phases based on temporal changes in testing protocols, vaccination availability, SARS-CoV-2 variant prevalence, and public health interventions. While other statistical methodologies may require fewer parameter specifications, joinpoint regression provided an interpretable framework for characterization and comparison of trends and trend turning points, revealing sewershed-specific variations in trend magnitude and timing that also aligned with known variant-driven waves. Week-level trend agreement corroborated previous findings demonstrating a close relationship between SARS-CoV-2 wastewater surveillance data and COVID-19 outcomes. These findings guide future applications of advanced statistical methodologies and support the continued integration of wastewater-based epidemiology as a complementary approach to traditional COVID-19 surveillance systems.