Analyzing SARS-CoV-2 Wastewater Surveillance Data for Public Health Impact in Southern Nevada

Background: Early in the COVID-19 pandemic, researchers determined that levels of severe acute respiratory coronavirus 2 (SARS-CoV-2) genetic material in wastewater could provide information about COVID-19 occurrence in communities. Wastewater-based epidemiology (WBE) efforts have subsequently expanded throughout the U.S. and internationally; the U.S. Centers for Disease Control and Prevention (CDC) initiated its National Wastewater Surveillance System (NWSS) in September of 2020 and continues its SARS-CoV-2 wastewater monitoring efforts following the end of the federal COVID-19 public health emergency in May 2023. The field of WBE has reached a critical juncture in determining the future of how SARS-CoV-2 wastewater surveillance data are translated into public health action. Gaps remain in the published literature regarding how to best identify trends and relate wastewater data to public health outcomes. Objective: This study examined SARS-CoV-2 wastewater surveillance data independently and in relation to COVID-19 confirmed cases over time in Southern Nevada with the goal of improving interpretations and public health applications of these data. Methods: Weekly, sewershed-specific SARS-CoV-2 RNA wastewater concentrations, along with local COVID-19 confirmed case data, from June 2020 through mid-April 2023 were included. In this study, Project I analyzed trends in these datasets using joinpoint regression and subsequently investigated the week-level agreement of those trends between variables. Projects II and III modeled the relationship between COVID-19 confirmed case counts and wastewater data in several ways, relying on the generalized estimating equation approach. Each of these analyses were also conducted in distinct local pandemic phases characterized by differences in SARS-CoV-2 testing, variants, vaccination, and disease control/response measures. Results: In Project I, joinpoint regression identified joinpoints and significant positive and negative trends in recovery-corrected and normalized wastewater concentrations, as well as the COVID-19 confirmed case rates, over time. Trends varied across sewersheds and phases but were consistent with expected variant-driven waves; notable agreement was observed between the wastewater and case rate trends. Project II established that there are significant, positive relationships of both recovery-corrected and normalized SARS-CoV-2 RNA concentrations in wastewater with COVID-19 confirmed case data, but these estimates showed some differences by study phase. Based on the selected models from Project II, Project III similarly found significant, positive relationships using the alternate wastewater metrics (two-week percentage change and percent of Omicron peak concentration) and when the case outcome was shifted in time. Discussion and Conclusion: This study affirmed the existence of significant, positive relationships between SARS-CoV-2 wastewater surveillance data and reported confirmed COVID-19 case data across sewersheds, though a phase effect was observed. These findings offer better understanding of the epidemiological and statistical relationship between wastewater concentrations and public health outcomes over time, helping inform how wastewater data are integrated into public health decision-making in the future, both in Southern Nevada and elsewhere. Continued research and investment in this area remain essential to maximize public health impact and bridge the communication gap between wastewater utilities, researchers, and public health agencies.

Publication Details

Journal
Digital Scholarship - UNLV (University of Nevada Reno)
Published
2025-02-28
DOI
https://doi.org/10.34917/38330369
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analyzing SARS-CoV-2 Wastewater Surveillance Data for Public Health Impact in Southern Nevada

Digital Scholarship - UNLV (University of Nevada Reno)
SARS-CoV-2 detection and testing
article

Analyzing SARS-CoV-2 Wastewater Surveillance Data for Public Health Impact in Southern Nevada

article en

Abstract

Background: Early in the COVID-19 pandemic, researchers determined that levels of severe acute respiratory coronavirus 2 (SARS-CoV-2) genetic material in wastewater could provide information about COVID-19 occurrence in communities. Wastewater-based epidemiology (WBE) efforts have subsequently expanded throughout the U.S. and internationally; the U.S. Centers for Disease Control and Prevention (CDC) initiated its National Wastewater Surveillance System (NWSS) in September of 2020 and continues its SARS-CoV-2 wastewater monitoring efforts following the end of the federal COVID-19 public health emergency in May 2023. The field of WBE has reached a critical juncture in determining the future of how SARS-CoV-2 wastewater surveillance data are translated into public health action. Gaps remain in the published literature regarding how to best identify trends and relate wastewater data to public health outcomes. Objective: This study examined SARS-CoV-2 wastewater surveillance data independently and in relation to COVID-19 confirmed cases over time in Southern Nevada with the goal of improving interpretations and public health applications of these data. Methods: Weekly, sewershed-specific SARS-CoV-2 RNA wastewater concentrations, along with local COVID-19 confirmed case data, from June 2020 through mid-April 2023 were included. In this study, Project I analyzed trends in these datasets using joinpoint regression and subsequently investigated the week-level agreement of those trends between variables. Projects II and III modeled the relationship between COVID-19 confirmed case counts and wastewater data in several ways, relying on the generalized estimating equation approach. Each of these analyses were also conducted in distinct local pandemic phases characterized by differences in SARS-CoV-2 testing, variants, vaccination, and disease control/response measures. Results: In Project I, joinpoint regression identified joinpoints and significant positive and negative trends in recovery-corrected and normalized wastewater concentrations, as well as the COVID-19 confirmed case rates, over time. Trends varied across sewersheds and phases but were consistent with expected variant-driven waves; notable agreement was observed between the wastewater and case rate trends. Project II established that there are significant, positive relationships of both recovery-corrected and normalized SARS-CoV-2 RNA concentrations in wastewater with COVID-19 confirmed case data, but these estimates showed some differences by study phase. Based on the selected models from Project II, Project III similarly found significant, positive relationships using the alternate wastewater metrics (two-week percentage change and percent of Omicron peak concentration) and when the case outcome was shifted in time. Discussion and Conclusion: This study affirmed the existence of significant, positive relationships between SARS-CoV-2 wastewater surveillance data and reported confirmed COVID-19 case data across sewersheds, though a phase effect was observed. These findings offer better understanding of the epidemiological and statistical relationship between wastewater concentrations and public health outcomes over time, helping inform how wastewater data are integrated into public health decision-making in the future, both in Southern Nevada and elsewhere. Continued research and investment in this area remain essential to maximize public health impact and bridge the communication gap between wastewater utilities, researchers, and public health agencies.

Digital Scholarship - UNLV (University of Nevada Reno)
Clean water and sanitation
Openalex Percentile: Top 50%
SARS-CoV-2 detection and testing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.