Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach
Abstract BackgroundDuring the COVID-19 pandemic, several US jurisdictions began to regularly report levels of SARS-CoV-2 in wastewater as a proxy for SARS-CoV-2 incidence. Despite the promise of this approach for improving COVID-19 situational awareness, the degree to which wa...
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JMIR Publications
2025-05-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2025/1/e68213 |
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| author | Masahiko Haraguchi Fayette Klaassen Ted Cohen Joshua A Salomon Nicolas A Menzies |
| author_facet | Masahiko Haraguchi Fayette Klaassen Ted Cohen Joshua A Salomon Nicolas A Menzies |
| author_sort | Masahiko Haraguchi |
| collection | DOAJ |
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Abstract
BackgroundDuring the COVID-19 pandemic, several US jurisdictions began to regularly report levels of SARS-CoV-2 in wastewater as a proxy for SARS-CoV-2 incidence. Despite the promise of this approach for improving COVID-19 situational awareness, the degree to which wastewater surveillance data agree with other data has varied, and better evidence is needed to understand the situations in which wastewater surveillance data track closely with traditional surveillance data.
ObjectiveIn this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions.
MethodsWe collated data on wastewater SARS-CoV-2 RNA levels and COVID-19 case reports from July 2020 to March 2023 for 107 counties representing a range in terms of geographic location, population size, and urbanicity. For these counties, we used Bayesian hierarchical regression modeling to estimate the statistical relationship between wastewater data and reported cases, allowing for variation in this relationship across counties. We compared different model structural approaches and assessed how the strength of the estimated relationships varied between settings and over time.
ResultsOur analyses revealed a strong positive relationship between wastewater data and COVID-19 cases for the majority of locations, with a median correlation coefficient between observed and predicted cases of 0.904 (IQR 0.823‐0.943). In total, 23/107 counties (21.5%) had correlation coefficients below 0.8, and 3/107 (2.8%) had values below 0.6. Across locations, the COVID-19 case rate associated with a given level of wastewater SARS-CoV-2 RNA concentration declined over the study period. Counties with greater population size (PP
ConclusionsIn a sample of 107 US counties, wastewater surveillance had a close relationship with COVID-19 cases reported for the majority of locations, with these relationships found to be stronger in counties with greater population size and urbanicity. In situations where routine COVID-19 surveillance data are less reliable, wastewater surveillance may be used to track local SARS-CoV-2 incidence trends. |
| format | Article |
| id | doaj-art-cc4b6526f7d140d48b67ed3c935009fa |
| institution | DOAJ |
| issn | 2369-2960 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
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| series | JMIR Public Health and Surveillance |
| spelling | doaj-art-cc4b6526f7d140d48b67ed3c935009fa2025-08-20T03:21:46ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602025-05-0111e68213e6821310.2196/68213Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling ApproachMasahiko Haraguchihttp://orcid.org/0000-0002-3250-5746Fayette Klaassenhttp://orcid.org/0000-0002-9488-2571Ted Cohenhttp://orcid.org/0000-0002-8091-7198Joshua A Salomonhttp://orcid.org/0000-0003-3929-5515Nicolas A Menzieshttp://orcid.org/0000-0002-2571-016X Abstract BackgroundDuring the COVID-19 pandemic, several US jurisdictions began to regularly report levels of SARS-CoV-2 in wastewater as a proxy for SARS-CoV-2 incidence. Despite the promise of this approach for improving COVID-19 situational awareness, the degree to which wastewater surveillance data agree with other data has varied, and better evidence is needed to understand the situations in which wastewater surveillance data track closely with traditional surveillance data. ObjectiveIn this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions. MethodsWe collated data on wastewater SARS-CoV-2 RNA levels and COVID-19 case reports from July 2020 to March 2023 for 107 counties representing a range in terms of geographic location, population size, and urbanicity. For these counties, we used Bayesian hierarchical regression modeling to estimate the statistical relationship between wastewater data and reported cases, allowing for variation in this relationship across counties. We compared different model structural approaches and assessed how the strength of the estimated relationships varied between settings and over time. ResultsOur analyses revealed a strong positive relationship between wastewater data and COVID-19 cases for the majority of locations, with a median correlation coefficient between observed and predicted cases of 0.904 (IQR 0.823‐0.943). In total, 23/107 counties (21.5%) had correlation coefficients below 0.8, and 3/107 (2.8%) had values below 0.6. Across locations, the COVID-19 case rate associated with a given level of wastewater SARS-CoV-2 RNA concentration declined over the study period. Counties with greater population size (PP ConclusionsIn a sample of 107 US counties, wastewater surveillance had a close relationship with COVID-19 cases reported for the majority of locations, with these relationships found to be stronger in counties with greater population size and urbanicity. In situations where routine COVID-19 surveillance data are less reliable, wastewater surveillance may be used to track local SARS-CoV-2 incidence trends.https://publichealth.jmir.org/2025/1/e68213 |
| spellingShingle | Masahiko Haraguchi Fayette Klaassen Ted Cohen Joshua A Salomon Nicolas A Menzies Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach JMIR Public Health and Surveillance |
| title | Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach |
| title_full | Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach |
| title_fullStr | Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach |
| title_full_unstemmed | Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach |
| title_short | Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach |
| title_sort | statistical relationship between wastewater data and case notifications for covid 19 surveillance in the united states from 2020 to 2023 bayesian hierarchical modeling approach |
| url | https://publichealth.jmir.org/2025/1/e68213 |
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