Wastewater SARS-CoV-2 RNA Concentration and Loading Variability from Grab and 24-Hour Composite Samples

  1. Kyle Curtis
  2. David Keeling
  3. Kathleen Yetka
  4. Allison Larson
  5. Raul Gonzalez

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Abstract

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires a significant, coordinated public health response. Assessing case density and spread of infection is critical and relies largely on clinical testing data. However, clinical testing suffers from known limitations, including test availability and a bias towards enumerating only symptomatic individuals. Wastewater-based epidemiology (WBE) has gained widespread support as a potential complement to clinical testing for assessing COVID-19 infections at the community scale. The efficacy of WBE hinges on the ability to accurately characterize SARS-CoV-2 RNA concentrations in wastewater. To date, a variety of sampling schemes have been used without consensus around the appropriateness of grab or composite sampling. Here we address a key WBE knowledge gap by examining the variability of SARS-CoV-2 RNA concentrations in wastewater grab samples collected every 2 hours for 72 hours compared with three corresponding 24-hour flow-weighted composite samples collected over the same period. Results show relatively low variability (respective means for N1, N2, N3 assays = 608, 847.9, 768.4 copies 100 mL -1 , standard deviations = 501.4, 500.3, 505.8 copies 100 mL -1 ) for grab sample concentrations, and good agreement between most grab samples and their respective composite (mean deviation from composite = 159 copies 100 mL -1 ). When SARS-CoV-2 RNA concentrations are used to calculate viral load (RNA concentration * total influent flow the sample day), the discrepancy between grabs (log 10 range for all grabs = 11.9) or a grab and its associated 24-hour composite (log 10 difference = 11.6) are amplified. A similar effect is seen when estimating carrier prevalence in a catchment population with median estimates based on grabs ranging 63-1885 carriers. Findings suggest that grab samples may be sufficient to characterize SARS-CoV-2 RNA concentrations, but additional calculations using these data may be sensitive to grab sample variability and warrant the use of flow-weighted composite sampling. These data inform future WBE work by helping determine the most appropriate sampling scheme and facilitate sharing of datasets between studies via consistent methodology.

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  1. Version published to 10.1101/2020.07.10.20150607v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.10.20150607: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    where; I = Estimated proportion of WWTP service area infected Data Analysis and Visualization: Data analysis and visualization was conducted using R Statistical Computing Software version 3.6.3.22 The dplyr23 and tidyr24 packages were primarily used for data manipulation and the ggplot2 package25 was used for all plotting.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations and Future Work: One important consideration for using WBE to examine viral trends during a pandemic is the heterogenous and dynamic nature of the spread of infections. Epidemiological work has shown that, particularly during the early stages of pathogen spread, rates of infection are not uniform but rather clustered in localized hotspots often driven by importation of cases26, and the disproportionate effects of “superspreading” events27. Interpreting WBE data is also confounded by transient use of the sewerage system from people who may be infected by do not live in the catchment area, e.g. tourists or people who commute to a different area for work. Restrictions such as stay-at-home orders and the subsequent reopening of cities add further complexity to the characteristics of viral spread in a community. As a result, extrapolation of findings from one catchment to the surrounding region are not often appropriate. Therefore, data and patterns presented here pertain to this specific catchment over a 3-day period, and do not easily extend to other areas or timeframes. To address this, we suggest a surveillance approach to WBE, monitoring multiple catchments on a routine basis17 to characterize trends specific to a region over time. As noted, variability in influent concentration change as density of cases increase or decrease within the catchment. Calculations using influent flow, such as viral load and carrier prevalence, will also be influenced by diel and seaso...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.10.20150607v1 on medRxiv