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Within-Day Variability of SARS-CoV-2 RNA in Municipal Wastewater Influent During Periods of Varying COVID-19 Prevalence and Positivity

Aaron Bivins1, Devin North1, Zhenyu Wu1, Marlee Shaffer1, Warish Ahmed2, Kyle Bibby1 •
University of Notre Dame1, Commonwealth Scientific and Industrial Research Organisation2
24 Mar 2021-medRxiv (Cold Spring Harbor Laboratory Press)-
TL;DR: In this paper, a sample of primary influent was performed every 2 hours over two different 24-hour periods at two wastewater treatment plants (WWTPs) in northern Indiana, USA.
Abstract: Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA is being used to monitor Coronavirus Disease 2019 (COVID-19) trends in communities; however, within-day variation in primary influent concentrations of SARS-CoV-2 RNA remain largely uncharacterized. In the current study, grab sampling of primary influent was performed every 2 hours over two different 24-hour periods at two wastewater treatment plants (WWTPs) in northern Indiana, USA. In primary influent, uncorrected, recovery-corrected, and pepper mild mottle virus (PMMoV)-normalized SARS-CoV-2 RNA concentrations demonstrated ordinal agreement with increasing clinical COVID-19 positivity, but not COVID-19 cases. Primary influent SARS-CoV-2 RNA concentrations exhibited greater variation than PMMoV RNA concentrations as expected for lower shedding prevalence. The bovine respiratory syncytial virus (BRSV) process control recovery efficiency was low (mean: 0.91%) and highly variable (coefficient of variation: 51% - 206%) over the four sampling events with significant differences between the two WWTPs (p <0.0001). The process control recovery was similar to the independently assessed SARS-CoV-2 RNA recovery efficiency, which was also significantly different between the two WWTPs (p <0.0001). Recovery-corrected SARS-CoV-2 RNA concentrations better reflected within-day changes in primary influent flow rate and fecal content, as indicated by PMMoV concentrations. These observations highlight the importance of assessing the process recovery efficiency, which is highly variable, using an appropriate process control. Despite large variations, both recovery-corrected and PMMoV-normalized SARS-CoV-2 RNA concentrations in primary influent demonstrate potential for monitoring COVID-19 positivity trends in WWTPs serving peri-urban and rural areas.

Summary (3 min read)

Jump to: [INTRODUCTION] – [(which was not certified by peer review)] – [Primary Influent Sampling Locations] – [24-h Sampling Experiments] – [Direct Extractions] – [RT-ddPCR] – [RT-ddPCR Assays] – [RNA Persistence Experiments] – [Process Control, Molecular Control & Concentration Recovery Efficiency] – [Within-Day Variation in Primary Influent] and [WWTP Influent SARS-CoV-2 RNA and Clinical Surveillance]

INTRODUCTION

  • When infected with severe acute coronavirus 2 (SARS-CoV-2), the β-coronavirus which causes coronavirus disease 2019 (COVID-19), humans, both symptomatic and asymptomatic 1 , shed the virus and its RNA, in various body fluids [2] [3] [4] including: sputum, saliva, urine, and feces.
  • Surveillance strategies and sampling methods for WBE remain diverse, with community-level temporal trends monitored via both primary solids 7, 8 and primary influent 9 .
  • Concentrations of SARS-CoV-2 RNA in wastewater and wastewater solids correlate with COVID-19 cases 8, 17, 18 and positivity rates 19 .
  • WBE studies of wastewater solids have reported improved correlation with clinical case trends 26 and no effect 8 associated with PMMoVnormalization, while a study of wastewater influent found that PMMoV-normalization decreased correlation with clinical case trends 18 . .

(which was not certified by peer review)

  • The copyright holder for this preprint this version posted March 24, 2021.
  • The first goal was to characterize the within-day variation in influent SARS-CoV-2 RNA concentrations, PMMoV RNA concentrations, and process control recovery efficiency.
  • The copyright holder for this preprint this version posted March 24, 2021.
  • During a methods comparison, significantly different recovery efficiencies were observed between a variety of process controls for a single method 42 .
  • BRSV RNA also demonstrated a similar increase over seven days at 4°C .

Primary Influent Sampling Locations

  • The experiments described herein were conducted at two WWTPs located in two communities, identified as community A and community B, in northern Indiana, USA.
  • Records from the Environmental Protection Agency's (EPA) Enforcement and Compliance History Online (ECHO) system indicate the design flow for each WWTP is 20 million gallons per day (MGD During each experiment, 500 mL primary influent grab samples were collected at 2-hour intervals and immediately stored at 4°C.
  • Samples were then transported on ice to the laboratory and again stored at 4°C until concentrated as described below within 24 hours.

24-h Sampling Experiments

  • While at WWTP B, a 24-hour time-based composite sample was only prepared using the grab samples from 1 December to 2 December.
  • The average hourly flow rates were recorded during each experiment and subsequently used to calculate average flow rates for each 2-hour interval and for the entire 24-hour experiment.

Direct Extractions

  • In addition to the primary influent samples concentrated by the adsorption-extraction method, a paired subset of 16 samples, eight collected from WWTP A and 8 from WWTP B (December 2020 experiments), were extracted by adding 500 µL of influent directly into a Garnet PowerBead tube and extracting the nucleic acids as described above.
  • The purpose of these direct extractions was to directly estimate the virus RNA concentration recovery efficiency by comparing the direct extraction enumerations and the adsorption-extraction enumerations.

RT-ddPCR

  • RNA in sample extracts was detected and quantified by RT-ddPCR performed on the BioRad QX200 Droplet Digital PCR System with thermal cycling performed on the C1000 Touch Thermal Cycler (BioRad, Hercules, CA, USA).
  • RNA reverse transcription and PCR amplification was performed in a single reaction using the One-Step RT-ddPCR Advanced Kit for Probes (BioRad, Hercules, CA, USA) per the manufacturer's instructions.
  • Primer and probe sequences, concentrations, and thermal cycling conditions for each RT-ddPCR assay are summarized in Table S1 .
  • Each RT-ddPCR experiment included notemplate controls, positive controls, and the pertinent negative extraction controls as described in further detail below.
  • The RNA copy number for each RT-ddPCR reaction was estimated by manual thresholding performed in QuantaSoft Version 1.7.4 (BioRad, Hercules, CA, USA) such that the negative controls, both no-template and extraction, were negative for each assay.

RT-ddPCR Assays

  • SARS-CoV-2 RNA was detected and quantified using the CDC N1 assay targeting the nucleocapsid gene 32 .
  • The N1 copy number in each sample was measured in triplicate RT-ddPCR reactions using the premixed primers and probe (Table S1 ) from the 2019-nCoV RUO Kit (IDT, Coralville, IA, USA).
  • At each step in the dilution series, 12 RT-ddPCR replicates were assayed.
  • A cumulative Gaussian distribution was fit to the observed proportion of positive technical replicates along the dilution series, and the 95% LOD was estimated as the 95th percentile of the resulting distribution.

RNA Persistence Experiments

  • In addition to the 24-h influent sampling experiments, a daily composite sample was collected from WWTP A on 23 June 2020, seeded with BRSV, and used to investigate the stability of RNA during storage, pasteurization, and freeze-thaw cycles.
  • To assess persistence during storage, the composite sample was aliquoted into 50 mL centrifuge tubes.
  • The tubes were incubated at either 4°C or 25°C with two tubes combined into a single 100 mL sample and processed every 24 hours from time zero to seven days.
  • Persistence through pasteurization was assessed by pasteurizing two 50 mL aliquots in centrifuge tubes at 60°C for 90 minutes, with a brief vortex mix at 45 minutes, and then combining the two aliquots into a single 100 mL sample.
  • After each thaw, two tubes were combined into one 100 mL sample for processing.

Process Control, Molecular Control & Concentration Recovery Efficiency

  • Across all experiments, 83 primary influent samples were concentrated by adsorption-extraction and assayed for SARS-CoV-2 and PMMoV RNA: 58 from WWTP A and 25 from WWTP B (one 24-hour event did not include a composite sample).
  • The observed recovery efficiency in samples from WWTP A was greater than in samples from WWTP B (p <0.0001); however, the coefficient of variation (CV) in samples from WWTP A was also greater (169%) than WWTP B (83%).
  • Interestingly, the mean recovery for wastewater seeded with Hep G was greater than for PCRgrade water seeded with Hep G (37%, n = 2).

Within-Day Variation in Primary Influent

  • During both 24-hour sampling intervals at WWTP A , hourly flow rates peaked from roughly 9:00 to midnight.
  • Summary statistics for each parameter measured during the 24-hour sampling events are listed in Table S2 .
  • PMMoV concentrations in primary influent at WWTP A were comparable between sampling events (p >0.9999), while PMMoV concentrations were greater during the May than December sampling in primary influent at WWTP B (p <0.0001).
  • During the May sampling event higher recoveries were observed during periods of both high and low flow.

WWTP Influent SARS-CoV-2 RNA and Clinical Surveillance

  • Due to the agreement between BRSV and SARS-CoV-2 RNA recovery, the effect of recoverycorrection on within-day trends, and the large variance associated with recovery efficiency, recovery-corrected SARS-CoV-2 RNA concentrations in primary influent were compared to county-level COVID-19 cases and positivity rates during the two weeks prior to each 24-hour sampling period.
  • SARS-CoV-2 RNA concentrations did not consistently increase with increasing average daily COVID-19 cases.
  • The results of the current study indicate that when sub-county level COVID-19 clinical surveillance data are not available, positivity may offer a better metric for comparison with wastewater data.
  • Differences in the primary influent concentrations during each of these periods were often not meaningfully different after accounting for variation in concentration and recovery.
  • These observations indicate that quantitative relationships between wastewater data and SARS-CoV-2 infection prevalence, particularly those premised on material balance, are likely to remain constrained by variability and uncertainty 21 .

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Content maybe subject to copyright    Report

Within-Day Variability of SARS-CoV-2 RNA in Municipal Wastewater Influent During
1
Periods of Varying COVID-19 Prevalence and Positivity
2
Aaron Bivins
1,2
, Devin North
1
, Zhenyu Wu
1
, Marlee Shaffer
1
, Warish Ahmed
3
, Kyle Bibby
1,2
*
3
1
Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame,
4
156 Fitzpatrick Hall, Notre Dame, IN 46556
5
2
Environmental Change Initiative, University of Notre Dame, 721 Flanner Hall, Notre Dame, IN
6
46556
7
3
CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Qld 4102, Australia
8
*kbibby@nd.edu
9
ABSTRACT
10
Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
11
RNA is being used to monitor Coronavirus Disease 2019 (COVID-19) trends in communities;
12
however, within-day variation in primary influent concentrations of SARS-CoV-2 RNA remain
13
largely uncharacterized. In the current study, grab sampling of primary influent was performed
14
every 2 hours over two different 24-hour periods at two wastewater treatment plants (WWTPs)
15
in northern Indiana, USA. In primary influent, uncorrected, recovery-corrected, and pepper mild
16
mottle virus (PMMoV)-normalized SARS-CoV-2 RNA concentrations demonstrated ordinal
17
agreement with increasing clinical COVID-19 positivity, but not COVID-19 cases. Primary
18
influent SARS-CoV-2 RNA concentrations exhibited greater variation than PMMoV RNA
19
concentrations as expected for lower shedding prevalence. The bovine respiratory syncytial
20
virus (BRSV) process control recovery efficiency was low (mean: 0.91%) and highly variable
21
(coefficient of variation: 51% - 206%) over the four sampling events with significant differences
22
between the two WWTPs (p <0.0001). The process control recovery was similar to the
23
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 24, 2021. ; https://doi.org/10.1101/2021.03.16.21253652doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
independently assessed SARS-CoV-2 RNA recovery efficiency, which was also significantly
24
different between the two WWTPs (p <0.0001). Recovery-corrected SARS-CoV-2 RNA
25
concentrations better reflected within-day changes in primary influent flow rate and fecal
26
content, as indicated by PMMoV concentrations. These observations highlight the importance of
27
assessing the process recovery efficiency, which is highly variable, using an appropriate
28
process control. Despite large variations, both recovery-corrected and PMMoV-normalized
29
SARS-CoV-2 RNA concentrations in primary influent demonstrate potential for monitoring
30
COVID-19 positivity trends in WWTPs serving peri-urban and rural areas.
31
Keywords: Wastewater-based Epidemiology, COVID-19, SARS-CoV-2, variability, primary
32
influent
33
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 24, 2021. ; https://doi.org/10.1101/2021.03.16.21253652doi: medRxiv preprint
INTRODUCTION
34
When infected with severe acute coronavirus 2 (SARS-CoV-2), the
β
-coronavirus which causes
35
coronavirus disease 2019 (COVID-19), humans, both symptomatic and asymptomatic
1
, shed
36
the virus and its RNA, in various body fluids
2–4
including: sputum, saliva, urine, and feces. Since
37
many of these body fluids are deposited into wastewater collection systems, wastewater-based
38
epidemiology (WBE) has emerged as a promising technique
5
for corroborating clinical
39
surveillance observations, or monitoring SARS-CoV-2 infection when clinical surveillance
40
systems are unavailable or limited
6
.
41
Surveillance strategies and sampling methods for WBE remain diverse, with community-level
42
temporal trends monitored via both primary solids
7,8
and primary influent
9
. Studies monitoring
43
primary influent for surveillance have used grab samples
10–14
, time-based composite
44
samples
15,16
, and flow-based composite samples
9,17
. Concentrations of SARS-CoV-2 RNA in
45
wastewater and wastewater solids correlate with COVID-19 cases
8,17,18
and positivity rates
19
.
46
Attempts to use wastewater data to estimate SARS-CoV-2 infection prevalence remain limited
47
due to large uncertainty and variation in shedding rates and viral sewershed dynamics
20,21
.
48
To reduce variation, normalization of SARS-CoV-2 RNA concentrations by pepper mild mottle
49
virus (PMMoV) RNA concentration has been suggested to account for the fecal content of
50
wastewater samples
8
. PMMoV is an elongated rod-shaped virus with a single-stranded
51
genome
22
that is prevalent in human feces
23,24
due to the consumption of produce and is
52
subsequently prevalent in wastewater globally
25
. WBE studies of wastewater solids have
53
reported improved correlation with clinical case trends
26
and no effect
8
associated with PMMoV-
54
normalization, while a study of wastewater influent found that PMMoV-normalization decreased
55
correlation with clinical case trends
18
.
56
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 24, 2021. ; https://doi.org/10.1101/2021.03.16.21253652doi: medRxiv preprint
The SARS-CoV-2 RNA concentration in municipal wastewater influent is expected to exhibit
57
temporal trends consistent with domestic sewage inputs and PMMoV influent concentration due
58
to the fecal shedding of SARS-CoV-2 RNA by those infected. This variability then drives best
59
sampling practices, e.g., grab versus composite samples. Studies of within-day variation in
60
SARS-CoV-2 RNA concentrations in primary influent remain limited. A recent study comparing
61
flow-weighted composites and grab samples found agreement between the two, but suggested
62
avoiding sampling during and immediately following early morning low flow periods due to low
63
concentrations from grab samples
27
. Another study hypothesized that a 10-fold increase in
64
SARS-CoV-2 RNA concentrations in flow-weighted influent composites compared to grab
65
samples suggested diurnal variation, but called for additional testing to confirm
28
. Additional
66
evidence is necessary to identify best sampling practices and inform data interpretation.
67
The purpose of the current study was to assess the variability associated with SARS-CoV-2
68
RNA in primary influent at two wastewater treatment plants (WWTPs) during distinct periods of
69
epidemic COVID-19. The effort had two primary goals. The first goal was to characterize the
70
within-day variation in influent SARS-CoV-2 RNA concentrations, PMMoV RNA concentrations,
71
and process control recovery efficiency. The second goal was to assess the relationships
72
between primary influent SARS-CoV-2 RNA concentration, including normalized concentrations,
73
and COVID-19 clinical surveillance metrics.
74
MATERIALS AND METHODS
75
Primary Influent Sampling Locations
76
The experiments described herein were conducted at two WWTPs located in two communities,
77
identified as community A and community B, in northern Indiana, USA. Records from the
78
Environmental Protection Agency’s (EPA) Enforcement and Compliance History Online (ECHO)
79
system indicate the design flow for each WWTP is 20 million gallons per day (MGD)
80
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 24, 2021. ; https://doi.org/10.1101/2021.03.16.21253652doi: medRxiv preprint
(https://echo.epa.gov/). The WWTP in community A (WWTP A) serves 56,227 residents and
81
had an average influent flow rate of 14.09 million gallons per day (MGD) in 2020 (250 gallons
82
per capita-day) while the WWTP in community B (WWTP B) serves 46,557 residents and had
83
an average influent flow rate of 11.50 MGD in 2020 (247 gallons per capita-day). Despite
84
serving fewer residents, the population density surrounding WWTP B is greater (2,995 persons
85
per square mile) than the density surrounding WWTP A (1,881 persons per square mile).
86
COVID-19 clinical surveillance data during the 14 days prior to each sampling period for the
87
counties A and B were obtained from the Indiana COVID-19 Dashboard and Map
88
(https://www.coronavirus.in.gov/2393.htm). COVID-19 clinical surveillance data at the sub-
89
county level are not publicly available for this region.
90
24-h Sampling Experiments
91
A total of four 24-hour sampling experiments were conducted: (1) WWTP A from 12:00 June 18
92
to 12:00 June 19, 2020; (2) WWTP A from 1:30 to 23:30 December 2; (3) WWTP B from 11:00
93
May 7 to 9:00 May 8, 2020; (4) WWTP B from 9:00 December 1 to 7:00 December 2, 2020.
94
During each experiment, 500 mL primary influent grab samples were collected at 2-hour
95
intervals and immediately stored at 4°C. Samples were then transported on ice to the laboratory
96
and again stored at 4°C until concentrated as described below within 24 hours. At WWTP A, 24-
97
hour time-based composite samples were also collected on 18 and 19 June and 2 December.
98
While at WWTP B, a 24-hour time-based composite sample was only prepared using the grab
99
samples from 1 December to 2 December. The average hourly flow rates were recorded during
100
each experiment and subsequently used to calculate average flow rates for each 2-hour interval
101
and for the entire 24-hour experiment.
102
Electronegative Membrane Adsorption and Extraction
103
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted March 24, 2021. ; https://doi.org/10.1101/2021.03.16.21253652doi: medRxiv preprint
Citations
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Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance

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Warish Ahmed1, Stuart L. Simpson1, Paul M. Bertsch1, Kyle Bibby2, Aaron Bivins2, Linda L. Blackall3, Sílvia Bofill-Mas4, Albert Bosch4, João Brandão5, Phil M. Choi6, Phil M. Choi7, Mark Ciesielski8, Erica Donner9, Nishita D'Souza10, Andreas H. Farnleitner11, Andreas H. Farnleitner12, Daniel Gerrity13, Raul Gonzalez, John F. Griffith14, Pradip Gyawali, Charles N. Haas15, Kerry A. Hamilton16, Hapuarachchige Chanditha Hapuarachchi17, Valerie J. Harwood18, Rehnuma Haque19, Greg Jackson6, Stuart J. Khan20, Wesaal Khan21, Masaaki Kitajima22, Asja Korajkic23, Giuseppina La Rosa24, Blythe A. Layton, Erin K. Lipp25, Sandra L. McLellan26, Brian R. McMinn23, Gertjan Medema, Suzanne Metcalfe1, Wim G. Meijer27, Jochen F. Mueller7, Heather M. Murphy28, Coleen C. Naughton29, Rachel T. Noble8, Sudhi Payyappat, Susan Petterson30, Tarja Pitkänen31, Tarja Pitkänen32, Verónica Beatriz Rajal33, Brandon Reyneke21, Fernando A. Roman29, Joan B. Rose10, Marta Rusiñol34, Michael J. Sadowsky, Laura Sala-Comorera27, Yin Xiang Setoh17, Samendra P. Sherchan35, Kwanrawee Sirikanchana36, Wendy Smith1, Joshua A. Steele14, Rosalie Subburg1, Erin M. Symonds18, Phong K. Thai7, Kevin V. Thomas7, Josh Tynan7, Simon Toze1, Janelle R. Thompson37, Andrew S. Whiteley1, Judith Chui Ching Wong17, Daisuke Sano38, Stefan Wuertz37, Stefan Wuertz39, Irene Xagoraraki10, Qian Zhang40, Amity G. Zimmer-Faust14, Orin C. Shanks23 •
Commonwealth Scientific and Industrial Research Organisation1, University of Notre Dame2, University of Melbourne3, University of Barcelona4, Instituto Nacional de Saúde Dr. Ricardo Jorge5, Queensland Health6, University of Queensland7, University of North Carolina at Chapel Hill8, University of South Australia9, Michigan State University10, Vienna University of Technology11, University of Health Sciences Antigua12, Southern Nevada Water Authority13, Southern California Coastal Water Research Project14, Drexel University15, Arizona State University16, National Environment Agency17, University of South Florida18, International Centre for Diarrhoeal Disease Research, Bangladesh19, University of New South Wales20, Stellenbosch University21, Hokkaido University22, United States Environmental Protection Agency23, Istituto Superiore di Sanità24, University of Georgia25, University of Wisconsin–Milwaukee26, University College Dublin27, University of Guelph28, University of California, Merced29, Griffith University30, University of Helsinki31, National Institute for Health and Welfare32, National University of Salta33, Spanish National Research Council34, Tulane University35, Chulabhorn Research Institute36, National University of Singapore37, Tohoku University38, Nanyang Technological University39, University of Minnesota40
20 Jan 2022-Science of The Total Environment
TL;DR: A technical review of factors that can lead to false-positive and -negative errors in the surveillance of SARS-CoV-2, culminating in recommendations and strategies that can be implemented to identify and mitigate these errors.

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Comparison of RT-qPCR and RT-dPCR Platforms for the Trace Detection of SARS-CoV-2 RNA in Wastewater

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TL;DR: The positivity results indicated that for the analysis of SARS-CoV-2 RNA in wastewater, including the eluate and pellet samples may further increase the detection sensitivity using RT-dPCR.
Abstract: We compared reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and RT digital PCR (RT-dPCR) platforms for the trace detection of SARS-CoV-2 RNA in low-prevalence COVID-19 locations in Queensland, Australia, using CDC N1 and CDC N2 assays. The assay limit of detection (ALOD), PCR inhibition rates, and performance characteristics of each assay, along with the positivity rates with the RT-qPCR and RT-dPCR platforms, were evaluated by seeding known concentrations of exogenous SARS-CoV-2 in wastewater. The ALODs using RT-dPCR were approximately 2–5 times lower than those using RT-qPCR. During sample processing, the endogenous (n = 96) and exogenous (n = 24) SARS-CoV-2 wastewater samples were separated, and RNA was extracted from both wastewater eluates and pellets (solids). The RT-dPCR platform demonstrated a detection rate significantly greater than that of RT-qPCR for the CDC N1 and CDC N2 assays in the eluate (N1, p = 0.0029; N2, p = 0.0003) and pellet (N1, p = 0.0015; N2, p = 0.0067) samples. The positivity results also indicated that for the analysis of SARS-CoV-2 RNA in wastewater, including the eluate and pellet samples may further increase the detection sensitivity using RT-dPCR.

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Averting an Outbreak of SARS-CoV-2 in a University Residence Hall through Wastewater Surveillance.

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Ryland Corchis-Scott1, Qiudi Geng1, Rajesh Seth1, Rajan Ray, Mohsan Beg, Nihar Biswas, Lynn Charron2, Kenneth D Drouillard1, Ramsey D'Souza, Daniel D. Heath1, Chris Houser1, Felicia Lawal, James McGinlay2, Sherri Lynne Menard3, Lisa A. Porter, Diane Rawlings2, Matthew L Scholl3, K. W. Michael Siu, Yufeng Tong, Christopher G. Weisener1, Steven W. Wilhelm4, Steven W. Wilhelm5, R. Michael L. McKay1, R. Michael L. McKay5 •
Great Lakes Institute of Management1, Residence Inn by Marriott2, University of Windsor3, University of Tennessee4, Bowling Green State University5
31 Oct 2021-Microbiology spectrum
TL;DR: In this paper, a wastewater surveillance program targeting a university residence hall was implemented during the spring semester 2021 as a proactive measure to avoid an outbreak of COVID-19 on campus, which resulted in actionable responses by university administration and public health.
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01 Mar 1947-Annals of Mathematical Statistics
TL;DR: In this paper, the authors show that the limit distribution is normal if n, n$ go to infinity in any arbitrary manner, where n = m = 8 and n = n = 8.
Abstract: Let $x$ and $y$ be two random variables with continuous cumulative distribution functions $f$ and $g$. A statistic $U$ depending on the relative ranks of the $x$'s and $y$'s is proposed for testing the hypothesis $f = g$. Wilcoxon proposed an equivalent test in the Biometrics Bulletin, December, 1945, but gave only a few points of the distribution of his statistic. Under the hypothesis $f = g$ the probability of obtaining a given $U$ in a sample of $n x's$ and $m y's$ is the solution of a certain recurrence relation involving $n$ and $m$. Using this recurrence relation tables have been computed giving the probability of $U$ for samples up to $n = m = 8$. At this point the distribution is almost normal. From the recurrence relation explicit expressions for the mean, variance, and fourth moment are obtained. The 2rth moment is shown to have a certain form which enabled us to prove that the limit distribution is normal if $m, n$ go to infinity in any arbitrary manner. The test is shown to be consistent with respect to the class of alternatives $f(x) > g(x)$ for every $x$.

11,055 citations

Journal Article•DOI•
Use of Ranks in One-Criterion Variance Analysis

[...]

William Kruskal1, W. Allen Wallis1•
University of Chicago1
01 Dec 1952-Journal of the American Statistical Association
TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.
Abstract: Given C samples, with n i observations in the ith sample, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C – 1), unless the samples are too small, in which case special approximations or exact tables are provided. One of the most important applications of the test is in detecting differences among the population means.* * Based in part on research supported by the Office of Naval Research at the Statistical Research Center, University of Chicago.

9,365 citations

Journal Article•DOI•
Temporal dynamics in viral shedding and transmissibility of COVID-19.

[...]

Xi He1, Eric H. Y. Lau2, Peng Wu2, Xilong Deng1, Jian Wang1, Xinxin Hao2, Yiu Chung Lau2, Jessica Y. Wong2, Yujuan Guan1, Xinghua Tan1, Xiaoneng Mo1, Yanqing Chen1, Baolin Liao1, Weilie Chen1, Fengyu Hu1, Qing Zhang1, Mingqiu Zhong1, Yanrong Wu1, Lingzhai Zhao1, Fuchun Zhang1, Benjamin J. Cowling2, Fang Li1, Gabriel M. Leung2 •
Guangzhou Medical University1, University of Hong Kong2
15 Apr 2020-Nature Medicine
TL;DR: It is estimated that 44% (95% confidence interval, 25–69%) of secondary cases were infected during the index cases’ presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home.
Abstract: We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector–infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 30–57%) of secondary cases were infected during the index cases’ presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission. Presymptomatic transmission of SARS-CoV-2 is estimated to account for a substantial proportion of COVID-19 cases.

3,943 citations

Journal Article•DOI•
Multiple Comparisons Using Rank Sums

[...]

Olive Jean Dunn1•
University of California, Los Angeles1
01 Aug 1964-Technometrics
TL;DR: In this paper, the use of rank sums from a combined ranking of k independent samples in order to decide which populations differ is suggested as a convenient alternative to making separate rankings for each pair of samples and the two methods are compared.
Abstract: This paper considers the use of rank sums from a combined ranking of k independent samples in order to decide which populations differ. Such a procedure is suggested as a convenient alternative to making separate rankings for each pair of samples, and the two methods are compared. Asymptotic use of the normal tables is given and the treatment of ties is discussed. A numerical example is given.

3,305 citations

Journal Article•DOI•
Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review.

[...]

Daniel P Oran1, Eric J. Topol1•
Scripps Health1
01 Sep 2020-Annals of Internal Medicine
TL;DR: In this article, the authors reviewed and synthesized the available evidence on asymptomatic SARS-CoV-2 infection and found that infected persons who remain as healthy played a significant role in the ongoing pandemic, but their relative number and effect have been uncertain.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful.

1,813 citations

Related Papers (5)
First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community.

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01 Aug 2020-Science of The Total Environment
Warish Ahmed, Nicola Angel, Janette Edson, Kyle Bibby, Aaron Bivins, Jake W. O'Brien, Phil M. Choi, Masaaki Kitajima, Stuart L. Simpson, Jiaying Li, Benjamin J. Tscharke, Rory Verhagen, Wendy Smith, Julian Zaugg, Leanne Dierens, Philip Hugenholtz, Kevin V. Thomas, Jochen F. Mueller 
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25 Aug 2020
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18 Sep 2020-Nature Biotechnology
Jordan Peccia, Alessandro Zulli, Doug E. Brackney, Nathan D. Grubaugh, Edward H. Kaplan, Arnau Casanovas-Massana, Albert I. Ko, Amyn A. Malik, Dennis Wang, Mike Wang, Joshua L. Warren, Daniel M. Weinberger, Wyatt Arnold, Saad B. Omer 
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27 May 2020-Environmental Science and Technology Letters
Gertjan Medema, Leo Heijnen, Goffe Elsinga, Ronald Italiaander, Anke Brouwer 
Comparison of approaches to quantify SARS-CoV-2 in wastewater using RT-qPCR: Results and implications from a collaborative inter-laboratory study in Canada

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04 Mar 2021-Journal of Environmental Sciences-china
Alex H.S. Chik, Alex H.S. Chik, Melissa B. Glier, Mark R. Servos, Chand S. Mangat, Xiao-Li Pang, Yuanyuan Qiu, Patrick M. D'Aoust, Jean-Baptiste Burnet, Robert Delatolla, Sarah Dorner, Qiudi Geng, John P. Giesy, Robert Mike McKay, Michael R. Mulvey, Michael R. Mulvey, Natalie Prystajecky, Natalie Prystajecky, Nivetha Srikanthan, Yuwei Xie, Bernadette Conant, Steve E. Hrudey 
Frequently Asked Questions (10)
Q1. What are the contributions in "Within-day variability of sars-cov-2 rna in municipal wastewater influent during periods of varying covid-19 prevalence and positivity" ?

It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. Despite large variations, both recovery-corrected and PMMoV-normalized 29 SARS-CoV-2 RNA concentrations in primary influent demonstrate potential for monitoring 30 COVID-19 positivity trends in WWTPs serving peri-urban and rural areas. 

electronegative membrane filtration methods have demonstrated 252 recoveries ranging from approximately 10% to less than 1% during virus concentration from 253 sewage42. 

The results of the current study indicate that when 437 sub-county level COVID-19 clinical surveillance data are not available, positivity may offer a 438 better metric for comparison with wastewater data. 

The large variation in recovery efficiency observed 376 during 24-hour sampling periods draws further attention to the importance the consistent use of 377 process controls in wastewater surveillance despite their limitations 

In the current study SARS-CoV-2 and PMMoV were recovered at different mean efficiencies 243 (1.9% and 19.2%, respectively) using the adsorption-extraction method. 

flow patterns during all the sampling events 455 were likely affected by changes in human behavior patterns associated with lockdowns and 456 interrupted domestic and working routines. 

Even with recovery adjustment, composite samples still yielded lower 365 SARS-CoV-2 RNA concentrations than the 24-hour average from grab samples. 

The sporadic use of process or molecular 382 controls observed in the WBE literature greatly limits the ability to compare SARS-CoV-2 RNA 383 measurements within and between WWTPs56. 

In a study of 262 SARS-CoV-2 adsorption to surfaces in solution, electrostatic adhesion correlated with both 263 solution ionic strength and surface chemistry49. 

For a subset of 217 four samples where the solids fraction was removed prior to adsorption-extraction, the BRSV 218 mean recovery efficiency was 25% (95%CI: 22 – 28).