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. 2022 Sep 16;17(9):e0270385. doi: 10.1371/journal.pone.0270385

Wastewater surveillance in smaller college communities may aid future public health initiatives

Laura Lee 1, Lescia Valmond 2, John Thomas 2, Audrey Kim 2, Paul Austin 1, Michael Foster 1, John Matthews 3, Paul Kim 2, Jamie Newman 1,*
Editor: Asli Aslan4
PMCID: PMC9481015  PMID: 36112629

Abstract

To date, the COVID-19 pandemic has resulted in over 570 million cases and over 6 million deaths worldwide. Predominant clinical testing methods, though invaluable, may create an inaccurate depiction of COVID-19 prevalence due to inadequate access, testing, or most recently under-reporting because of at-home testing. These concerns have created a need for unbiased, community-level surveillance. Wastewater-based epidemiology has been used for previous public health threats, and more recently has been established as a complementary method of SARS-CoV-2 surveillance. Here we describe the application of wastewater surveillance for SARS-CoV-2 in two university campus communities located in rural Lincoln Parish, Louisiana. This cost-effective approach is especially well suited to rural areas where limited access to testing may worsen the spread of COVID-19 and quickly exhaust the capacity of local healthcare systems. Our work demonstrates that local universities can leverage scientific resources to advance public health equity in rural areas and enhance their community involvement.

Introduction

Since the COVID-19 pandemic was declared, there have been over 570 million infections and over 6 million deaths worldwide [1]. Over the past two years, mutations during viral replication coupled with the unchecked global spread of COVID-19 have led to the emergence of more transmissible variants of concern. The first of these variants, the novel SARS-CoV-2 B.1.617.2 (Delta), was identified in India in December 2020 [2]. This variant was the catalyst for a COVID-19 surge seen in July 2020 [3]. Similarly, the novel SARS-CoV-2 B.1.1.529 (Omicron) variant emerged in November 2021 and resulted in yet another surge and a record number of cases across the United States [4].

Rapid diagnostic testing is a critical tool for breaking viral transmission chains and provides data on the prevalence and spread of infectious diseases that can inform public health decision making. However, in the case of COVID-19, each surge was exacerbated by limited supply and access to testing in the US, meaning that often the reports were underestimating the number of infected individuals. More at-home testing and more mild or asymptomatic cases due to acquired immunity have further widened the discrepancy between caseload reporting and actual infections [5]. All of this then points to a need for additional community surveillance of SARS-CoV-2.

Wastewater-based epidemiology (WBE), used for decades to monitor chemicals and pathogens through the analysis of sewage, has been propelled into the spotlight during the pandemic as a complementary tool for estimating COVID-19 prevalence in a community [6–9]. Compared to large-scale diagnostic testing programs, WBE avoids bias, is non-invasive, and is less constrained by limited testing capacity [10]. Although the conversion of viral RNA copy number in sewage to infected individuals is complicated by biological and sewershed variability [11], WBE can still capture near-real-time longitudinal trends. Importantly, WBE has been shown to predict case surges by approximately 5–14 days, providing opportunities for public health and epidemiologic intervention [12, 13].

Rural areas that have fewer resources than urban areas have lagged in testing rates while also being home to a more vulnerable population [14, 15]. In a low testing environment, a WBE approach is especially useful as it indicates infection levels and encourages allocation of resources to those communities to prevent or at least minimize the impact of an outbreak. Previously, WBE has been implemented on various campuses where sufficient clinical testing is not feasible, providing a basis for SARS-CoV-2 monitoring and outbreak mitigation [16–19]. Here we report on the analysis of longitudinal samples collected throughout the Delta and Omicron surges in rural Lincoln Parish, Louisiana. We assess the effect of fecal normalization and compare temporal trends of SARS-CoV-2 in the wastewater to confirmed cases to estimate the sensitivity of wastewater surveillance. To our knowledge, this is the first study of its kind where two public, rural primarily undergraduate campuses leveraged limited resources to forge partnerships and produce data to inform public health.

Methods

Wastewater from the city of Ruston was collected and analyzed at Louisiana Tech University (LTU) and wastewater from the city of Grambling and the Grambling State University campus was collected and analyzed at Grambling State University (GSU). The same protocol was followed by the two laboratories whenever possible with any differences described below.

Wastewater sample collection

City of Ruston wastewater

Wastewater samples were collected from the single wastewater treatment facility in Ruston, Louisiana, the Ruston Water Treatment Plant. A total of 2.4 L of wastewater was collected with a refrigerated autosampler over 12 hours from 7:00 am to 7:00 pm on the day of sampling. From that, a 100 mL composite sample (2 50 mL tubes) was collected, and heat inactivated in a water bath at 60°C for 90 minutes with one turn at 45 minutes. This was done to adhere to safety protocols required for bringing wastewater back to the university laboratory space. Literature indicates that this is not required as the virus viability is greatly reduced in stool samples [20]. Following inactivation, these samples were stored at 4°C to be picked up that same week. Composite samples were collected from 5/26/2021 to 5/4/2022 and processed at LTU.

City of Grambling and GSU campus wastewater

The lift stations most proximal to the City of Grambling Wastewater Treatment Plant that convey wastewater from the city sewershed (32.516403, −92.717004) and GSU campus sewershed (32.515078, −92.718722) were selected for weekly sampling. Composite samples (150 mL per hour for 24 hours) were collected on ice from Tuesday morning to Wednesday morning each week from 4/27/2021 to 5/3/2022 and immediately processed at GSU.

Wastewater sample processing

City of Ruston wastewater

The heat-inactivated wastewater samples were centrifuged at 4696 × g for 30 minutes to remove debris. The 2 50 mL samples were combined into one 20 mL aliquot of supernatant, and viral matter was precipitated using 10% polyethylene glycol (PEG) and 2.25% NaCl with gentle inversion until reagents dissolved based on the method described by Hebert [21]. This solution was stored at 4°C until centrifugation at 12,000 × g for 120 minutes at 4°C. The resulting pellet was resuspended in 140 μL nuclease-free water and stored at −80°C until RNA extraction could be completed.

City of Grambling and GSU campus wastewater

Viruses were concentrated from 60 mL of clarified supernatant via PEG/NaCl precipitation as at LTU. The wastewater/PEG/NaCl solution was centrifuged at 12,000 × g for 99 minutes at 4°C. The resulting pellet was resuspended in 140 μL of PBS for immediate extraction.

Viral RNA extraction

City of Ruston wastewater

Viral RNA was extracted from 140 μL of resuspended samples using the QIAGEN QIAmp MiniKit (#52904) according to the manufacturer’s protocol. The extracted RNA was eluted from the column using 40 μL elution buffer. The RNA purity was determined using the BioTek Cytation Take5 plate reader with A260/A280 ratios averaging 1.7–2.2.

City of Grambling and GSU campus wastewater

Viral RNA was extracted as at LTU with one modification. Carrier RNA (5.6 μg) was added to the lysis buffer of all samples except the initial sample to allow for quality assessment of the extracted RNA. RNA integrity measured using the Invitrogen Qubit RNA IQ Assay indicated 61% large or structured RNA and 39% small RNA.

RT-qPCR and fecal normalization

City of Ruston wastewater

10 μL of viral RNA was used in a 20 μL reaction to create cDNA using the Applied Biosystems High-Capacity Reverse Transcription Kit with RNAse Inhibitor (#4368814) according to the manufacturer’s protocol. The resulting cDNA was stored at −20°C for up to a week prior to quantification of SARS-CoV-2 RNA presence. SARS-CoV-2 presence was measured via qPCR detection using the IDT 2019-nCoV RUO Kit (#10006713) containing the CDC 2019-nCoV diagnostic primer/probe mixes for the N1 and N2 gene targets (IDT #10006625) with the TaqMan Universal PCR Master Mix (Thermo Fisher #4304437) according to the manufacturer’s protocol. Each reaction contained 10 μL master mix, 1.5 μL primer/probe mix for N1 or N2, 2 μL target sample or no-template control, and was brought to a total volume of 20 μL using nuclease-free water. Reactions were run at 95°C for 10 minutes, 50 cycles of 95°C for 15 seconds followed by 60°C for 1 minute. For qPCR detection of PMMoV, a primer/probe mix previously described by Haramoto et al. was used instead of the N1 or N2 primer/probe mixes [22]. Amplification parameters were 25°C for 10 minutes, 95°C for 3 minutes, 45 cycles of 95°C for 15 seconds followed by 60°C for 1 minute. All qPCR reactions were done in triplicate. The reactions were prepared in an Applied Biosystems MicroAmp Fast 96 well reaction plate (#4346906) sealed with MicroAmp clear optical adhesive film (#4311971) and analyzed on an Applied Biosystems StepOnePlus RT-qPCR machine. N1 and N2 samples were quantified using a serial dilution for each gene target. IDT 2019-nCoV N positive control plasmid (#10006625) was used at concentrations ranging from 4 × 105 to 4 × 101 copies per reaction. PMMoV samples were quantified using a 68 bp DNA oligo containing the target region in a serial dilution ranging from 2.4 × 107 to 2.4 × 101 copies per reaction [21]. For fecal normalization, the genome copies or GC/mL of N1 and N2 were divided by the GC/mL of PMMoV to obtain a unitless ratio of SARS-CoV-2 to PMMoV [23]. Molecular-grade water was used as a method blank to validate protocol and technique. No amplification was observed in the method blank or in the no-template controls. The limit of detection defined as 95% probability of detection was estimated using logistic regression at 11.48 GC/reaction. Non-detect replicates were excluded and only quantifiable replicates were used in subsequent analysis.

City of Grambling and GSU campus wastewater

Reverse transcription was performed as at LTU. The cDNA was stored at −20°C for 1 to 3 days prior to analysis for SARS-CoV-2 and 1 to 8 weeks prior to analysis for PMMoV. SARS-CoV-2 and PMMoV were quantified as at LTU using the IDT 2019-nCoV RUO kit but with the IDT PrimeTime Gene Expression Master Mix (#1055772) according to the manufacturers’ protocols. All qPCR reactions were assembled in triplicate. Samples were prepared in a Bio-Rad HSP9601 clear well plate sealed with a Bio-Rad MSB1001 adhesive optical film and analyzed on a Bio-Rad CFX Connect instrument. Quantification cycle (Cq) was determined using Bio-Rad CFX Manager 3.1. N1 and N2 in the samples were quantified using serial dilutions of two standards: (1) the IDT 2019-nCoV_N positive control plasmid containing the complete nucleocapsid gene at concentrations ranging from 2 × 104 to 2 × 101 plasmid copies per reaction and (2) the ATCC VR3276SD synthetic RNA, reverse transcribed following the same protocol as sample RNA, at concentrations ranging from 2 × 104 to 2 × 101 RNA copies input to reverse transcription (Fig 1). PMMoV quantification and normalization was performed as described at LTU.

Fig 1.

Fig 1

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Standard curves obtained from IDT 2019-nCoV_N positive control plasmid (left) and reverse-transcribed ATCC VR3276SD synthetic RNA (right).

Pepper mild mottle virus optimization for copy number normalization

Plasmid standards have been reported to overestimate the viral load due to inefficient amplification of the supercoiled template during earlier cycles. In our study, the synthetic RNA-derived standards were slightly less efficient (N1 93.7%, N2 90.1%) thus the positive control plasmid was used for quantification of gene copies (Fig 1). PMMoV in the samples was quantified using serial dilutions of a 68 bp DNA oligo containing the target region at concentrations ranging from 2.4 x 107 to 2.4 x 101 copies per reaction.

The N1 primer set and the positive control plasmid generated a standard with an R2 value of 0.978 and 97.8% efficiency (slope −3.39, intercept 42.71). The N2 primer set and the positive control plasmid generated a standard with an R2 value of 0.991 and 95.9% efficiency (slope −3.46, intercept 43.549). The PMMoV primer set generated a standard with an R2 value of 0.996 and 88.7% efficiency (slope −3.65, intercept 42.15).

The presence of PCR inhibitors in the sample matrix was tested by spiking 2 x 105 copies of the IDT 2019-nCoV_N positive control plasmid into extracted wastewater in which no SARS-CoV-2 was detected and comparing the Cq to the same concentration of plasmid in the standard. There was no significant difference in Cq between the wastewater extract (24.87) and standard (24.76) suggesting no PCR inhibition.

Results

Ruston, a city in rural Lincoln Parish, Louisiana with a population of approximately 22,000 people, is home to Louisiana Tech University, a public university with an enrollment of approximately 12,000 students. The city of Ruston has a single wastewater treatment facility that services wastewater for over 90% of the population. To carry out wastewater surveillance of SARS-CoV-2, we collaborated with the city’s wastewater treatment facility to obtain samples for analysis. Samples were not able to be collected every week due to inclement weather, critical mechanical difficulties at the treatment facility, or absence of staff (S1 Table). The city of Grambling, also in Lincoln Parish, has a population of 5,150 residents as of the July 8, 2021, census. Grambling State University (GSU) has an enrollment of 5,438 students with 2,005 students living on campus and 226 faculty and 367 staff members working on campus during the Fall 2021 academic term, and 1,818 students, 197 faculty, and 374 staff members on campus during the Spring 2022 academic term.

We determined the concentration (genome copies or GC/L) of pepper mild mottle virus (PMMoV), a fecal indicator that is frequently used to normalize wastewater testing and account for fluctuations in population or precipitation during the collection period (Fig 2). PMMoV is highly abundant in raw wastewater with concentrations ranging from 105 to 109 GC/L typically being reported in the literature [24]. We detected PMMoV in all samples from all sites with average concentrations in the order of 108 GC/L in Ruston and 106 GC/L in the smaller Grambling community. In Grambling, the PMMoV concentrations in the city mirrored the GSU campus, which in turn were highly dependent on the academic calendar with a high of 2.7 × 107 GC/L detected during Homecoming week and a low of 2.4 × 103 GC/L detected during the Thanksgiving Break. PMMoV concentrations in Ruston were not as coupled to the LTU academic calendar and various events in the community that brought people to Ruston, LA may account for spikes in the wastewater signal.

Fig 2. Pepper mild mottle virus (PMMoV) in wastewater.

Fig 2

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PMMoV concentrations expressed as genome copies or GC/L in the wastewater of Ruston (A), Grambling (B), and Grambling State University (C). The timeline is annotated with key events and dates including dates when no wastewater samples were collected or PMMoV amplification failed. For full table of reporting in Ruston see S1 Table.

The non-normalized wastewater concentrations of SARS-CoV-2 in Ruston, Grambling, and GSU are expressed as GC/L in Fig 3. In Ruston, N1 or N2 genes were detected in 45 of 46 wastewater samples with values ranging from 1 × 103 GC/L to 1.1 × 106 GC/L. In January and February 2022, the concentrations of SARS-CoV-2 detected were unexpectedly low considering this was during the peak of the Omicron surge in Louisiana. In Grambling, N1 or N2 was only detected in 19 of 51 wastewater samples and at much lower concentrations than in Ruston, often only exceeding the limit of detection when viral loads were relatively high in the GSU campus sewershed. On the GSU campus, we observed two spikes in the wastewater signal associated with the Delta and Omicron surges against a low baseline signal in 29 of 51 wastewater samples.

Fig 3. SARS-CoV-2 in wastewater.

Fig 3

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N1 and N2 concentrations expressed as genome copies or GC/L in the wastewater of Ruston (A), Grambling (B), and Grambling State University (C).

The PMMoV-normalized wastewater concentrations of SARS-CoV-2 expressed as unitless ratios are presented with city caseload data (Fig 4). Normalizing for fecal load reveals that SARS-CoV-2 concentrations in Ruston wastewater during the Omicron surge were comparable to those detected during the Delta surge. The low GC/L observed in the non-normalized data may have been due to viral losses in the sewage system. In the Grambling community, there was little correlation between normalized SARS-CoV-2 concentration in city wastewater and confirmed infections in the city as both appear to be primarily driven by the influx and efflux of people on the GSU campus. Because SARS-CoV-2 can be shed in feces early in the course of COVID-19 infection, it has been proposed that wastewater surveillance can serve as an early warning system [25–27]. On the GSU campus however, the wastewater signal appeared to lag or at best coincide with the increase in confirmed infections during the Delta surge. The sudden influx of thousands of students at the beginning of the academic year, all of whom were screened if moving into campus housing, precludes using wastewater surveillance as a forecasting tool in this instance. There was no data collected from the campus sewershed during Winter Break, but it is reasonable to expect a similar lack of predictive power during the initial Omicron surge which coincided with the return of students to campus. Other limitations of our study include the lack of a matrix control to assess viral recovery and the lack of normalization for daily wastewater flow.

Fig 4. PMMoV-normalized SARS-CoV-2 in wastewater and weekly caseloads.

Fig 4

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N1 and N2 concentrations in the wastewater of Ruston (A), Grambling (B), and Grambling State University (C) were divided by PMMoV concentrations to obtain a unitless ratio that normalizes for fecal load. All ratios are relative to the lowest ratio set arbitrarily as 100 and plotted on the left Y axis. Total weekly caseloads in zip codes 71270 and 71273 (Ruston) and zip code 71245 (Grambling/Grambling State University) are plotted on the right Y axis.

Conclusion

Here we demonstrate the ability of smaller universities to serve as public health resources in their community by engaging undergraduate students in wastewater surveillance. Monitoring of wastewater for SARS-CoV-2 is especially critical as we enter a time of at-home testing and generally less official reporting. This is a trend confirmed by data from The Institute for Health Metrics and Evaluation, our analysis of which suggests that during the peak of the Delta surge, approximately 41% of cases are estimated to have been reported, compared to roughly 21% during the peak of the Omicron surge, and less than 10% toward the end of this study period [28]. Incorporation of wastewater surveillance at college campuses, as seen at GSU, is crucial as university cases have been shown to influence the case numbers and characteristics of the broader community in which they are located [29]. Ruston, Grambling, and GSU all saw an increase in viral wastewater concentrations in January and February 2022, corresponding to increased regional and national caseloads. However, in looking at the data (Fig 4), the same relative genome copies corresponded to higher local caseloads in earlier months than what was being reported in April and May 2022, suggesting more recent under-reporting of COVID-19 cases in communities. This is a significant problem because under-reporting may lead to a false sense of security among the public and hinder data-driven decisions by policymakers. Overall, this study indicates the need to continue regular surveillance and heed the warnings of viral genome concentrations in the wastewater as a representative indicator of community health as well as COVID-19 cases in the area [6, 30–33]. Smaller communities do not always have access to the same resources or information available in larger cities. In these cases, it is critical that the university community become engaged in monitoring and supporting public health initiatives. Integration of facilities designed for wastewater surveillance in low income and rural communities is possible and could enhance understanding of public health [34]. The ability for two campuses to initiate this type of surveillance and train undergraduate students to be a part of the research programs establishes a model for this type of work going forward that will allow universities to participate in public health.

Supporting information

S1 Table. Description of collection and/or detection failure in Ruston, LA WBE.

(TIF)

Click here for additional data file. (467.6KB, tif)

Acknowledgments

We would like to thank Helaina Desentz, Louisiana Department of Health, Region 8 Epidemiologist for providing local caseload data and Casey Jackson for wastewater sampling through the duration of this project.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

JJN, PK Rockefeller Foundation Regional Accelerator PK received an. Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P2O GM103424-20 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  • 34.Bivins A, Kaya D, Bibby K, Simpson SL, Bustin SA, Shanks OC, et al. Variability in RT-qPCR assay parameters indicates unreliable SARS-CoV-2 RNA quantification for wastewater surveillance. Water Research. 2021. Sep 15;203:117516. doi: 10.1016/j.watres.2021.117516 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Asli Aslan

26 Jul 2022

PONE-D-22-16566Wastewater surveillance in smaller college communities may aid future public health initiatives

PLOS ONE

Dear Dr. Newman,

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PLOS ONE

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors of the manuscript PONE-D-22-16566 have conducted a nice study but there are some loopholes in the manuscript which needs to be fixed before accepted for publication. The points to be addressed are mentioned below;

Please reframe the 1st sentence of the abstract. The part “since its declaration” doesn’t sound good. Maybe the sentence can be reframed to “Since the declaration of COVID-19 pandemic” or any other of your choice.

In the introduction, please cite references where WBE has been imposed at school/ college/ university level. Few of the references are provided below;

https://doi.org/10.1371/journal.pone.0270168

https://doi.org/10.1016/j.scitotenv.2021.146408

https://doi.org/10.1016/j.scitotenv.2021.146749

Please highlight the novelty of the study in the last paragraph of the introduction.

Why was the ww sample from one WWTP heat inactivated but other one not?

Was there any field blank, method blank, extraction blank?

What about negative controls?

What are the QA/QC measures taken into consideration?

Was the LoD, LoQ determined?

Was any RT-qPCR inhibition noticed?

Incorporate statistical analysis.

Critical scientific explanation for the obtained results are missing.

Please provide better picture resolution of the figures and the fonts should be legible.

Please incorporate references in support of the obtained results. The literatures provided below when cited would enhance the manuscript;

https://doi.org/10.1371/journal.pone.0266407

https://doi.org/10.1128/aem.01740-21

https://doi.org/10.1016/j.scitotenv.2021.152503

https://doi.org/10.1016/j.scitotenv.2021.150264

https://doi.org/10.1371/journal.pwat.0000007

https://doi.org/10.1021/acsestwater.2c00052

Reviewer #2: This is a well-written report of SARS-CoV-2 wastewater monitoring conducted at three rural locations. The analysis is clearly reported and follows one conventional viral concentration procedure that has been commonly used despite its analytical challenges and drawbacks (e.g., <0.51% recovery of phi6 used as an indicator for SARS-CoV-2 in this paper: https://www.sciencedirect.com/science/article/pii/S0048969721058009 )

My primary concern is that the PEG precipitation method does not necessarily appear to generate concentrations that provide reliable detection or trend information. For example, the authors report low concentrations in the wastewater during the Omicron surge, lack of correlation with observed clinical case trends, and lag of wastewater data (rather than lead over clinical cases). Some of these observations could be explained by poor clinical testing or other environmental factors, but it’s also possible some of the observations result from analytical issues. The analysis appears to have been performed carefully and consistently, so I wonder how effective this particular method actually is at low concentrations (which may be most relevant in these communities). Generally, we’ve found the PEG precipitation method to perform poorly. The authors should provide more context in terms of the success (or not) of the PEG method applied in generating reliable results that can be used to capture trends in infection rates for similar contexts. If other studies have demonstrated the particular utility of this method, this would build confidence in the wastewater results reported. The normalization with PMMoV also does not appear to improve the correlations with clinical cases—is this consistent with what others have seen using this method in similar contexts? Were correlations between wastewater and clinical cases in this study better or worse with and without PMMoV normalization?

I absolutely agree that rural areas and small communities can benefit from wastewater monitoring, but more sensitive and reliable methods may be needed to confidently report results (e.g., analysis of primary clarifier sludge is much more sensitive as an overall method for detection https://pubs.rsc.org/en/content/articlehtml/2022/ew/d1ew00826a ). If the wastewater measurements don’t consistently lead over clinical cases, the tool is not useful for early warning. Only analytical results (non-normalized) from WWTP plant (Figure 2C) appears to have consistent detection for N1 and N2 as well as trends that fit with expectations from the known surges in infections. Can the authors explain the poor agreement between N1 and N2 in Figure 2a?

I recommend that the authors report the full suite of Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to improve the completeness of the reported results ( for further information, see Bivins et al, 2021 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341816/ ).

Especially given higher rates of non-detects, the limit of detection (LOD) in terms of genome copies / ul extract as well as genome copies/ ml wastewater sample. Standard curves are shown, but I don’t believe the LOD was reported. How non-detects were treated in triplicate qPCR analysis should also be reported (e.g., if 1 or more results of the triplicate were below the LOD, were the replicates assigned a value such as the LOD, were the results censored, or how else where they handled?).

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2022 Sep 16;17(9):e0270385. doi: 10.1371/journal.pone.0270385.r002

Author response to Decision Letter 0

6 Aug 2022

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Style requirements have been reviewed and followed

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Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

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We will update your Data Availability statement to reflect the information you provide in your cover letter.

Data has been uploaded as Supporting Information files.

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Captions have been added as is appropriate to Supporting Information files.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors of the manuscript PONE-D-22-16566 have conducted a nice study but there are some loopholes in the manuscript which needs to be fixed before accepted for publication. The points to be addressed are mentioned below;

Please reframe the 1st sentence of the abstract. The part “since its declaration” doesn’t sound good. Maybe the sentence can be reframed to “Since the declaration of COVID-19 pandemic” or any other of your choice.

In the introduction, please cite references where WBE has been imposed at school/ college/ university level. Few of the references are provided below;

https://doi.org/10.1371/journal.pone.0270168

https://doi.org/10.1016/j.scitotenv.2021.146408

https://doi.org/10.1016/j.scitotenv.2021.146749

We appreciate the suggestions for additional reference of WBE in academic institutions and the suggested citations. The abstract has been edited and relevant citations added to the introduction.

Please highlight the novelty of the study in the last paragraph of the introduction.

The introduction has been modified to reflect the fact that, to our knowledge, this was the first study done across two rural, primarily undergraduate university campuses that leveraged limited resources to forge partnerships and produce data that may inform public health in the future.

Why was the ww sample from one WWTP heat inactivated but other one not?

Many labs do not heat inactive wastewater samples and heat inactivation has been shown to have no significant impact on viral detection. As the studies reported in our manuscript were performed on two different campuses, with different safety protocols, the wastewater for one site was heat treated at the treatment facility before being taken to the university research lab in order to adhere to university safety protocol. Pecson BM et al. Environ Sci.: Water Res. Technol. 2021

Was there any field blank, method blank, extraction blank?

Molecular grade water was used in the initial extraction reactions and resulted in no amplification. To conserve resources, this was not repeated with each reaction once confirmation of protocol was obtained. This has been noted in the methods.

What about negative controls?

No template controls were used as negative controls. This has been noted in the methods section.

What are the QA/QC measures taken into consideration?

As described in methods, RNA quality was first assessed in the absence of carrier RNA using an RNA IQ assay and identified 61% long, structured RNA. We also measured A260/A280 ratios in the range of 1.7 to 2.2, indicating acceptable purity of the extracted RNA, and this has been added to the methods.

Was the LoD, LoQ determined?

The LOD was determined using logistic regression and the method has been updated.

Was any RT-qPCR inhibition noticed?

No inhibition was noticed. This was previously mentioned in the supplemental information and has been moved to the methods section for clarification.

Incorporate statistical analysis.

We have elaborated on analysis, quality control, and technique validation in our methods section.

Critical scientific explanation for the obtained results are missing.

We are not sure what the reviewer is asking for with this comment. We have attempted to clearly explain all of our data, provide literature references to support our findings and conclusions, and addressed all other comments to enhance the manuscript for publication.

Please provide better picture resolution of the figures and the fonts should be legible.

The figures were exported at high resolution (600 dpi) and in the upload of files it was noted they will not appear high resolution in the pdf compiled for review. This should be addressed in the final proof of the paper.

Please incorporate references in support of the obtained results. The literatures provided below when cited would enhance the manuscript;

https://doi.org/10.1371/journal.pone.0266407

https://doi.org/10.1128/aem.01740-21

https://doi.org/10.1016/j.scitotenv.2021.152503

https://doi.org/10.1016/j.scitotenv.2021.150264

https://doi.org/10.1371/journal.pwat.0000007

https://doi.org/10.1021/acsestwater.2c00052

We appreciate this suggestion. Additional references have been added to support the results of the study.

Reviewer #2: This is a well-written report of SARS-CoV-2 wastewater monitoring conducted at three rural locations. The analysis is clearly reported and follows one conventional viral concentration procedure that has been commonly used despite its analytical challenges and drawbacks (e.g., <0.51% recovery of phi6 used as an indicator for SARS-CoV-2 in this paper: https://www.sciencedirect.com/science/article/pii/S0048969721058009 )

My primary concern is that the PEG precipitation method does not necessarily appear to generate concentrations that provide reliable detection or trend information. For example, the authors report low concentrations in the wastewater during the Omicron surge, lack of correlation with observed clinical case trends, and lag of wastewater data (rather than lead over clinical cases). Some of these observations could be explained by poor clinical testing or other environmental factors, but it’s also possible some of the observations result from analytical issues. The analysis appears to have been performed carefully and consistently, so I wonder how effective this particular method actually is at low concentrations (which may be most relevant in these communities). Generally, we’ve found the PEG precipitation method to perform poorly. The authors should provide more context in terms of the success (or not) of the PEG method applied in generating reliable results that can be used to capture trends in infection rates for similar contexts. If other studies have demonstrated the particular utility of this method, this would build confidence in the wastewater results reported. The normalization with PMMoV also does not appear to improve the correlations with clinical cases—is this consistent with what others have seen using this method in similar contexts? Were correlations between wastewater and clinical cases in this study better or worse with and without PMMoV normalization?

We appreciate the reviewer’s concerns over some aspects of methodology. We agree that PEG is generally not as robust as other methods. Although there are some reports of higher recoveries of enveloped viruses in wastewater using PEG than other methods (e.g. of phi6 or of CHV) (Flood MT et al. Food Envrion Virol. 2021; Barril PA et al. Sci Total Environ. 2021). Other studies indicate similar performance (e.g. 44% PEG vs 28%-65% other methods) (Ahmed W et al. Sci Total Environ. 2020) or varying reductions in recovery. Given the literature supporting the efficacy of the technique, PEG precipitation does appear to be appropriate for the circumstances presented.

The use of PPMoV for normalization does help to better analyze the data at the 3 sites. This is most apparent in the non-normalized data for Ruston which does not follow the clinical case load during the peak of Omicron in January 2022 but does correlate after normalization. This normalization is critical to the study as there are variables including rainfall that may affect concentration of virus in a given sample. The greatest challenge is likely not in the methodology, but in missed sample dates and irregular sampling intervals that result from a reliance on student and city worker schedules. Despite those missing data points, the trends in viral load correlate with reported caseloads. Finally, given the nature of wastewater testing and college towns, there is a regular fluctuation in the population that can account for discrepancies in wastewater detection, timing of detection, and correlation to caseloads. We agree that this is a valid point when assessing city wastewater in college towns and so additional text has been added to the conclusion to highlight this critical aspect of the study.

I absolutely agree that rural areas and small communities can benefit from wastewater monitoring, but more sensitive and reliable methods may be needed to confidently report results (e.g., analysis of primary clarifier sludge is much more sensitive as an overall method for detection https://pubs.rsc.org/en/content/articlehtml/2022/ew/d1ew00826a ). If the wastewater measurements don’t consistently lead over clinical cases, the tool is not useful for early warning. Only analytical results (non-normalized) from WWTP plant (Figure 2C) appears to have consistent detection for N1 and N2 as well as trends that fit with expectations from the known surges in infections. Can the authors explain the poor agreement between N1 and N2 in Figure 2a?

We agree that as a predictive tool, wastewater testing has limitations. However, as we entered a time of at-home testing, over all less testing, and in turn less reporting, wastewater-based epidemiology provides a critical accounting of virus or other detectable pathogens in a community. This is especially useful in areas where testing is limited, where there is a large population of lower income families who may not test as often for fear of missing work, and in communities where policies have been relaxed and so again, there is less testing and reporting to public health officials. The outcome of this is illustrated in Figure 2C where the wastewater data shows high levels of virus circulating in the community despite few clinical cases being reported. This coincided with relaxed policies and at-home testing where people likely had COVID-19 and were either not testing and/or not reporting to public health officials. The problem of dashboards underreporting cases and instilling a false sense of security is increasingly recognized. This points to the necessity of this type of consistent monitoring in all communities in order to best understand disease prevalence and inform public health. As pointed out, the data reported in Figure 2A does show different levels of amplification for N1 and N2. The CDC N1 primer probe set has been reported to be more sensitive than the N2 primer probe set with the N2 more likely to result in higher Cq or non-detect in a clinical setting. An assessment of 36 laboratories testing wastewater also concluded that the N1 primer probe results in higher concentrations than N2. Whether this is inherent to the primer probe sequences or due to the stability of the target regions or both is unclear but we present both sets of data. (Pecson BM et al. Environ Sci.: Water Res Technol. 2021)

I recommend that the authors report the full suite of Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to improve the completeness of the reported results ( for further information, see Bivins et al, 2021 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341816/ ).

Some of the essential details in the MIQE checklist related to reagent/buffer compositions, target/oligonucleotide sequences can be derived from our detailed description of the materials and kits used. We have explicitly added information related to nucleic acid and qPCR QA/QC such as method blank, RNA purity, result of NTC, LOD.

Especially given higher rates of non-detects, the limit of detection (LOD) in terms of genome copies / ul extract as well as genome copies/ ml wastewater sample. Standard curves are shown, but I don’t believe the LOD was reported. How non-detects were treated in triplicate qPCR analysis should also be reported (e.g., if 1 or more results of the triplicate were below the LOD, were the replicates assigned a value such as the LOD, were the results censored, or how else where they handled?).

We have addressed these concerns in our methods section. The LOD is reported and the handling of non-detects is described.

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Decision Letter 1

Asli Aslan

2 Sep 2022

Wastewater surveillance in smaller college communities may aid future public health initiatives

PONE-D-22-16566R1

Dear Dr. %Newman%,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Asli Aslan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

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Acceptance letter

Asli Aslan

7 Sep 2022

PONE-D-22-16566R1

Wastewater surveillance in smaller college communities may aid future public health initiatives

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on behalf of

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    Supplementary Materials

    S1 Table. Description of collection and/or detection failure in Ruston, LA WBE.

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