Timing and Trends for Municipal Wastewater, Lab-Confirmed Case, and Syndromic Case Surveillance of COVID-19 in Raleigh, North Carolina

Kotlarz, Nadine; Holcomb, David; Pasha, Akbar; Reckling, Stacie; Kays, Judith; Lai, Y. M.; Daly, Sean W.; Palani, Sivaranjani; Bailey, Erika; Guidry, Virginia; Christensen, Ariel; Berkowitz, Steven; Hoppin, Jane A.; Mitášová, Helena; Engel, Lawrence S.; Reyes, Francis L. de los; Harris, Angela

doi:10.2105/ajph.2022.307108

Cited by 8 publications

(12 citation statements)

References 29 publications

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“…6 Yet adoption of wastewater surveillance to inform public policy has not yet become widespread, in part because of challenges in interpreting results with shifting detection methods, virus strains, populations served, and wastewater dynamics. 7 To date, available data evaluating wastewater metrics against cases or hospitalizations in the US are geographically limited, evaluating a single sewershed 8,9 or a few sewersheds grouped regionally. 10 The CDC's National Wastewater Surveillance System (NWSS) collates data from a majority of currently operating wastewater testing sites in the US.…”

Section: Introductionmentioning

confidence: 99%

Use of Wastewater Metrics to Track COVID-19 in the US

et al. 2023

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ImportanceWidespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence.ObjectiveTo examine the association of county-level wastewater metrics with high case and hospitalization rates nationwide both before and after widespread use of at-home tests.Design, Setting, and ParticipantsThis observational cohort study with a time series analysis was conducted from January to September 2022 in 268 US counties in 22 states participating in the US Centers for Disease Control and Prevention’s National Wastewater Surveillance System. Participants included the populations of those US counties.ExposuresCounty level of circulating SARS-CoV-2 as determined by metrics based on viral wastewater concentration relative to the county maximum (ie, wastewater percentile) and 15-day percentage change in SARS-CoV-2 (ie, percentage change).Main Outcomes and MeasuresHigh county incidence of COVID-19 as evidenced by dichotomized reported cases (current cases ≥200 per 100 000 population) and hospitalization (≥10 per 100 000 population lagged by 2 weeks) rates, stratified by calendar quarter.ResultsIn the first quarter of 2022, use of the wastewater percentile detected high reported case (area under the curve [AUC], 0.95; 95% CI, 0.94-0.96) and hospitalization (AUC, 0.86; 95% CI, 0.84-0.88) rates. The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the first 3 quarters of 2022. The Youden index for detecting high case rates was wastewater percentile of 51% (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95). A model inclusive of both metrics performed no better than using wastewater percentile alone. The performance of wastewater percentile declined over time for cases in the second quarter (AUC, 0.84; 95% CI, 0.82-0.86) and third quarter (AUC, 0.72; 95% CI, 0.70-0.75) of 2022.Conclusions and RelevanceIn this study, nationwide, county wastewater levels relative to the county maximum were associated with high COVID-19 case and hospitalization rates in the first quarter of 2022, but there was increasing dissociation between wastewater and clinical metrics in subsequent quarters, which may reflect increasing underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments. This study offers a strategy to operationalize county wastewater percentile to improve the accurate assessment of community SARS-CoV-2 infection prevalence when reliability of conventional surveillance data is declining.

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Section: Introductionmentioning

confidence: 99%

Use of Wastewater Metrics to Track COVID-19 in the US

et al. 2023

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“…Each estimate was also classified as increasing or decreasing according to the sign of the point estimate (positive or negative, respectively, as the point estimate was never exactly zero). 18 The rate of change estimate at observation i was considered a true positive (TP) when both the point estimate and true rate of change were positive ; a true negative (TN) for and ; a false positive (FP) for ; and a false negative (FN) for but . We assessed binary classification performance by sensitivity, the proportion of true increasing trends correctly classified as increasing, and specificity, the proportion of true decreasing trends correctly classified as decreasing: CDC suggests a third class, plateau, corresponding to low rates of change that may not warrant a response.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…18 Rate of change estimates from both approaches have been compared with population-based metrics (e.g., reported cases) but, to the best of our knowledge, the estimation performance and trend classification accuracy of either approach has not yet been evaluated. 18,20 We developed a simulation-based approach to evaluate rate of change estimates using synthetic time series data for which the underlying smooth trends and their rates of change were known exactly.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Rates of Change to Interpret Quantitative Wastewater Surveillance of Disease Trends

Holcomb,

Christensen,

Hoffman

et al. 2024

Preprint

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BackgroundWastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.ObjectivesWe assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.MethodsSmooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021 – May 2024 at 25 sites in North Carolina, USA.ResultsAll four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.DiscussionOur results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

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“…Yet adoption of wastewater surveillance to inform public policy has not yet become widespread, in part due to challenges interpreting results with shifting detection methods, strains, populations served, and wastewater dynamics 7 . To date, available data evaluating wastewater metrics against cases or hospitalizations in the US are geographically limited, evaluating a single sewershed 8,9 or a few sewersheds grouped regionally 10 . The CDC's National Wastewater Surveillance System (NWSS) collates data from a majority of currently operating wastewater testing sites in the US 11 .…”

Section: Introductionmentioning

confidence: 99%

Use of wastewater metrics to track COVID-19 in the U.S.: a national time-series analysis over the first three quarters of 2022

Varkila

Montez‐Rath

Salomon

et al. 2023

Preprint

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Background: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Using nationwide data available through the US National Wastewater Surveillance System, we examined the performance of two wastewater metrics in predicting high case and hospitalizations rates both before and after widespread use of at-home tests. Methods: We performed area under the receiver operating characteristic (ROC) curve analysis (AUC) for two wastewater metrics- viral concentration relative to the peak of January 2022 ('wastewater percentile') and 15-day percent change in SARS-CoV-2 ('percent change'). Dichotomized reported cases (equal to or greater than 200 or <200 cases per 100,000) and new hospitalizations (equal to or greater than 10 or <10 per 100,000) were our dependent variables, stratified by calendar quarter. Using logistic regression, we assessed the performance of combining wastewater metrics. Results: Among 268 counties across 22 states, wastewater percentile detected high reported case and hospitalizations rates in the first quarter of 2022 (AUC 0.95 and 0.86 respectively) whereas the percent change did not (AUC 0.54 and 0.49 respectively). A wastewater percentile of 51% maximized sensitivity (0.93) and specificity (0.82) for detecting high case rates. A model inclusive of both metrics performed no better than using wastewater percentile alone. The predictive capability of wastewater percentile declined over time (AUC 0.84 and 0.72 for cases for second and third quarters of 2022). Conclusion: Nationwide, county wastewater levels above 51% relative to the historic peak predicted high COVID rates and hospitalization in the first quarter of 2022, but performed less well in subsequent quarters. Decline over time in predictive performance of this metric likely reflects underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments.

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Timing and Trends for Municipal Wastewater, Lab-Confirmed Case, and Syndromic Case Surveillance of COVID-19 in Raleigh, North Carolina

Cited by 8 publications

References 29 publications

Use of Wastewater Metrics to Track COVID-19 in the US

Use of Wastewater Metrics to Track COVID-19 in the US

Estimating Rates of Change to Interpret Quantitative Wastewater Surveillance of Disease Trends

Use of wastewater metrics to track COVID-19 in the U.S.: a national time-series analysis over the first three quarters of 2022

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