SARS-CoV-2 concentration in wastewater consistently predicts trends in COVID-19 case counts by at least two days across multiple WWTP scales

Swift, Candice L.; Isanovic, Mirza; Velez, Karlen E. Correa; Norman, R. Sean

doi:10.1016/j.envadv.2023.100347

Cited by 10 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the limiting factors of our dataset, it was not possible to determine the exact length of the anticipation afforded by the wastewater data. However, other studies have reported that wastewater data precede clinical case detection by 0–14 days [ 39 , [67] , [68] , [69] , [70] , [71] ]. This broad range suggests that the anticipation period is dynamic over time and space [ 46 , 70 ] and is affected by multiple factors including the strains in circulation and the vaccine coverage [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

Majumdar,

Taye,

Bjornberg

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

Majumdar,

Taye,

Bjornberg

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The 7-day moving average based on case counts on the 7 days leading up to the sampling date was determined. 7-day moving averages have given good correlations in other studies. ,− Samples where the measured SARS-CoV-2 RNA concentrations were below the LOQ (set at 15 copies per reaction) were excluded from statistical analysis to reduce noise in the data . SARS-CoV-2 levels were normalized by dividing the viral concentration in copies per L by normalizing factors (copies per L of F+, or % recovery of BCoV), or, in the case of load normalization, by multiplying the viral concentration by the influent flow rate (L/day).…”

Section: Materials and Methodsmentioning

confidence: 99%

View of an Evolving Pandemic: Changes in the Relationship Between Clinical Cases and Levels of SARS-CoV-2 RNA in Colorado Wastewater

Ghanbari,

Huang,

Luc

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

The utility and interpretation of SARS-CoV-2 wastewater monitoring data as a predictor of community health can be confounded by variables such as wastewater system complexity, viral variants, and human behavior, including vaccination status and use of at-home tests. Here, we explored the relationship between the COVID caseload and SARS-CoV-2 concentration in wastewater for 23 locations over a 22-month period as the pandemic evolved. Spearman’s rank analysis showed strong correlations (ρ > 0.7) for most facilities, independent of normalization. While correlations remained strong throughout the pandemic, application of change point analysis (CPA) identified shifts in the relationship between reported clinical cases and the wastewater signal over time. These shifts did not generally coincide with known pandemic milestones, suggesting the involvement of multiple interacting or unknown variables. Models accounting for these shifts in the pandemic phase showed significantly improved predictions of reported caseloads. Additionally, the existence of change points highlights the increased reliability of wastewater data over clinical data when changes in the ratio of cases to wastewater concentrations are due to changes in human immunity and behaviors. In future pandemics, public health professionals will ideally be aware that the case-to-copy ratio can change unpredictably as pandemics evolve, and CPA can support public health decision-making.

show abstract

“…An emerging technology that promises improve surveillance capabilities pathogen detection in wastewater [1]. This has been shown to provide early detection of epidemic trends compared to other surveillance sources [2][3][4][5][6]. The technology has potential to detect a range human diseases [7][8][9][10][11][12][13] and reach otherwise overlooked communities [14,15].…”

mentioning

confidence: 99%

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

Colman

Kao

2023

Preprint

View full text Add to dashboard Cite

Background: Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods: We introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. Results: The real-time growth rate of the SARS-CoV-2 prevalence is estimated at 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions data. Conclusion: We provide a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.

show abstract

SARS-CoV-2 concentration in wastewater consistently predicts trends in COVID-19 case counts by at least two days across multiple WWTP scales

Cited by 10 publications

References 35 publications

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

View of an Evolving Pandemic: Changes in the Relationship Between Clinical Cases and Levels of SARS-CoV-2 RNA in Colorado Wastewater

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

Contact Info

Product

Resources

About