tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2025 May 22:11:e68213.
doi: 10.2196/68213. Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach
Masahiko Haraguchi[SUP] 1 [/SUP], Fayette Klaassen[SUP] 1 [/SUP], Ted Cohen[SUP] 2 [/SUP], Joshua A Salomon[SUP] 3 [/SUP], Nicolas A Menzies[SUP] 1 [/SUP]
Affiliations
Background: During 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.
Objective: In this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions.
Methods: We 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.
Results: Our 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 (P<.001) and higher levels of urbanicity (P<.001) had stronger concordance between wastewater data and COVID-19 cases. Measures of model fit, and relationships with urbanicity and population size, were robust to sensitivity analyses in which we varied the time period of analysis and the sample of counties used for model fitting.
Conclusions: In 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.
Keywords: Bayesian; Bayesian modeling; COVID-19; COVID-19 surveillance; SARS-CoV-2; USA; United States of America; coronavirus; emission; environment; environmental surveillance; hierarchical model; infectious; pandemic; pulmonary; regression model; respiratory; sewer; sewerage; surveillance; wastewater; wastewater epidemiology.
. 2025 May 22:11:e68213.
doi: 10.2196/68213. Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach
Masahiko Haraguchi[SUP] 1 [/SUP], Fayette Klaassen[SUP] 1 [/SUP], Ted Cohen[SUP] 2 [/SUP], Joshua A Salomon[SUP] 3 [/SUP], Nicolas A Menzies[SUP] 1 [/SUP]
Affiliations
- PMID: 40402554
- DOI: 10.2196/68213
Background: During 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.
Objective: In this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions.
Methods: We 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.
Results: Our 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 (P<.001) and higher levels of urbanicity (P<.001) had stronger concordance between wastewater data and COVID-19 cases. Measures of model fit, and relationships with urbanicity and population size, were robust to sensitivity analyses in which we varied the time period of analysis and the sample of counties used for model fitting.
Conclusions: In 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.
Keywords: Bayesian; Bayesian modeling; COVID-19; COVID-19 surveillance; SARS-CoV-2; USA; United States of America; coronavirus; emission; environment; environmental surveillance; hierarchical model; infectious; pandemic; pulmonary; regression model; respiratory; sewer; sewerage; surveillance; wastewater; wastewater epidemiology.