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Variability of Clinical Metrics in Small Population Communities Drive
Perceived Wastewater and Environmental Surveillance Data Quality:
Ontario, Canada-Wide Study
Nada Hegazy, K. Ken Peng, Patrick M. D’Aoust, Lakshmi Pisharody, Elisabeth Mercier,
Nathan Thomas Ramsay, Md Pervez Kabir, Tram Bich Nguyen, Emma Tomalty, Felix Addo,
Chandler Hayying Wong, Shen Wan, Joan Hu, Charmaine Dean, Minqing Ivy Yang, Hadi Dhiyebi,
Elizabeth A. Edwards, Mark R. Servos, Gustavo Ybazeta, Marc Habash, Lawrence Goodridge,
Art F. Y. Poon, Eric J. Arts, Stephen Brown, Sarah Jane Payne, Andrea Kirkwood,
Denina Bobbie Dawn Simmons, Jean-Paul Desaulniers, Banu Ormeci, Christopher Kyle, David Bulir,
Trevor Charles, R. Michael McKay, K. A. Gilbride, Claire Jocelyn Oswald, Hui Peng,
Christopher DeGroot, WSI Consortium, Elizabeth Renouf, and Robert Delatolla*
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ACCESS Metrics & More Article Recommendations *
sı Supporting Information
ABSTRACT: The emergence of COVID-19 in Canada has led to over
4.9 million cases and 59,000 deaths by May 2024. Traditional clinical
surveillance metrics (hospital admissions and clinical laboratory-positive
cases) were complemented with wastewater and environmental
monitoring (WEM) to monitor SARS-CoV-2 incidence. However,
challenges in public health integration of WEM persist due to perceived
limitations of WEM data quality, potentially driving inconsistent
correlations variability and lead times. This study investigates how
factors like population size, WEM measurement magnitude, site isolation
status, hospital admissions, and clinical laboratory-positive cases aect
WEM data correlations and variability in Ontario. The analysis uncovers
a direct relationship between clinical surveillance data and the population
size of the surveyed sewersheds, while WEM measurement magnitude
was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely
reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with
thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-
laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination
immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight
the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides
insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially
other diseases.
KEYWORDS: WEM data, population, hospital admissions, laboratory-positive cases, small communities, variability
1. INTRODUCTION AND BACKGROUND
The emergence of the novel 2019 coronavirus (COVID-19) in
Canada on January 25th, 2020
1
has resulted in over 4.9 million
confirmed cases and more than 59,000 COVID-19 deaths by
May, 2024.
2
In response to this public health crisis, population-
wide SARS-CoV-2 diagnosis using polymerase chain reaction
(PCR) has proven eective in identifying outbreaks and
informing public health decisions.
3
Utilizing similar analytical
methods, wastewater and environmental monitoring (WEM)
has emerged as a valuable population-wide tool for monitoring
and providing an early indication of COVID-19 incidence.
4−8
By the time of writing this manuscript (May 2024), the global
landscape of WEM for COVID-19 has spanned across more
than 72 countries, encompassing over 4600 monitoring sites.
9
Received: October 4, 2024
Revised: February 7, 2025
Accepted: February 10, 2025
Published: March 7, 2025
Articlepubs.acs.org/estwater
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This article is licensed under CC-BY-NC-ND 4.0
WEM not only plays a significant role in real-time monitoring of
SARS-CoV-2 infection rates,
6,8,10
but also extends its reach to
the surveillance of other diseases at a population level, including
influenza,
11−13
respiratory syncytial virus (RSV),
14−16
and
Mpox.
11,17
The Ontario Wastewater Surveillance Initiative (Ontario
WSI) is a Canadian wastewater surveillance network that
oversaw up to 107 sites at its peak.
18,19
The Ontario WSI began
initial surveillance targeting SARS-CoV-2, and eventually
expanded testing to include Influenza subtypes A and B, and
RSV, with all data being accessible to public health agencies
across the province. The initiative was inaugurated on January
first, 2021, by the Government of Ontario, led by the Ministry of
the Environment, Conservation and Parks (MECP) and
supported by the Ontario Wastewater Surveillance Consortium
(OWSC). Following a strategic sampling plan initiated on April
first, 2023, resources were focused on community sites that
reliably represent unique populations and geographical areas
across Ontario, resulting in 59 sites being monitored across the
province, covering 60% of Ontario’s residents. The initiative was
ocially closed on July 31st, 2024. The Ontario WSI monitored
the province of Ontario, which has a population size of
approximately 15.6 million people, representing 38.5% of the
nation’s residents.
20
The province has reported 34.0% of
Canada’s total reported COVID-19 infections, with more than
1.6 million confirmed COVID-19 cases,
21
necessitating
extensive sample processing facilitated through collaborations
with 13 academic institutions across the province.
18
Notably,
Ontario encompasses 19 census metropolitan areas.
22
Of the
total 107 sampling locations, 25 sites were used to survey
populations exceeding 100,000 inhabitants, while the remaining
82 sites were dedicated to serving areas with less than 100,000
inhabitants.
Assessment of the relationship between wastewater SARS-
CoV-2 viral signal and clinical surveillance metrics, particularly
COVID-19-caused hospital admissions and laboratory-con-
firmed positive cases, was critical to interpret and validate the
WEM results. This assessment is typically achieved by evaluating
Spearman’s rank correlation coecient (ρ) between the WEM
data and clinical surveillance metrics (COVID-19-caused
hospital admissions and laboratory-confirmed positive cases)
and determining the lead time of the wastewater surveillance
data. As the COVID-19 pandemic evolved, many countries
reduced population-wide PCR testing with the widespread
availability of at-home COVID-19 rapid antigen testing, leading
to a reduced correlation between WEM data and laboratory-
confirmed positive cases.
23−27
Eective December 31st, 2021,
Ontario’s PCR testing eligibility shifted from population-wide
access to focus on symptomatic and high-risk individuals.
28
Consequently, the Ontario WSI program progressively inte-
grated hospital admissions data, geospatially linked to the
specific sewersheds (areas draining via a sanitary sewer network
to the sampling point) under surveillance, as an essential
comparator for validating the trends observed in WEM data
measurements through the WEM-hospital admission correla-
tion.
Despite the rapid expansion of WEM over the past four years,
there remains a significant gap in its integration and actionable
use by public health ocials and policymakers in Canada and
worldwide.
4,29,30
This gap is primarily due to concerns about the
quality of WEM data. Additionally, the inherent variability of the
WEM data and inconsistencies in the lead times between WEM
data and clinical surveillance metrics further complicate its
perceived reliability.
31−35
During the surge of the two prominent
waves in Ontario caused by the B.1.1.529.1 Variant of Concern
(VOC) (first Omicron BA.1 sublineage, hereafter called
Omicron BA.1, predominant in Canada from December 2021
to March 2022) and the B.1.1.529.2 VOC (second Omicron
BA.2 sublineage, hereafter called Omicron BA.2, dominant in
Canada from March 2022 to July 2022), inconsistencies in the
Spearman’s rank correlation coecient (ρ) between WEM data,
both in copies per liter of wastewater sample collected (hereafter
referred to as cp/L) and in copies per copies of pepper mild
mottle virus (PMMoV) (hereafter referred to as cp/cp) and
clinical surveillance metrics (hospital admissions and laboratory-
positive cases), were revealed within the Ontario WSI program.
Furthermore, a wastewater surveillance study across 55 sampling
locations in the U.S. from April 2020 to May 2021 also displayed
similarly inconsistent Spearman’s correlations between WEM
data and laboratory-positive cases, as ref. no. 32 reported. WEM
data is also perceived to be highly variable due to the dierences
in WEM site characteristics or methodologies, such as
population mobility, sampling location in the network, methods
of sample collection, reporting standards, and high standard
deviations in viral signal concentration measurements.
31,35
Across Canada and worldwide, WEM was found to lead clinical
surveillance data by 3 to 14 days. This range in lead time is
attributed to various factors, such as wastewater to laboratory
travel time, population immunity dynamics, VOC onset, and
climatic conditions.
6,24,33,34,36−38
This lack of consistency in the
correlation, perceived high variability of WEM data, and
inconsistent lead times between WEM data and clinical metrics
across sites has largely been attributed to assumed limitations of
WEM. This assumption in turn has led to the mistrust or delayed
trust of WEM viral signal data by health decision-makers and has
directly limited the actionability of WEM data in Canada and
globally,
39,40
which continues to pose a significant challenge in
the field of WEM and public health. Inconsistencies in
correlations between WEM and traditional clinical surveillance
metrics may not, however, be entirely attributable to WEM data
sets and instead may be due to disparities in clinical data
reporting, along with the absence of consistent standards of
reporting of traditional clinical surveillance data.
1,41
The specific factors driving the observed range of correlations
between WEM data and clinical surveillance metrics in Ontario,
and in WEM programs around the world, remain unclear,
particularly during periods of high transmission like the
Omicron BA.1 and BA.2 waves. Understanding these factors is
crucial, particularly in regions with limited healthcare infra-
structure, and where the need for WEM extends beyond
COVID-19 to monitor other viruses such as influenza, RSV,
Mpox, and other diseases. This study hypothesizes that
population size, the magnitude of WEM measurements, site
isolation status (proximity to metropolitan centers), and
limitations in clinical surveillance metrics, including daily
range and the proportion of zeros, influence the quality of
correlation between WEM viral signal data (in cp/L and cp/cp)
and clinical surveillance data (hospital admissions and
laboratory-positive cases). Furthermore, this study investigates
the variability of both WEM viral signal data (in cp/L and cp/
cp) and clinical surveillance data to determine their role in
shaping correlation qualities. It is noted that the laboratory-
positive cases are likely to be underreported during the period
between the Omicron BA.1 and BA.2 waves due to the change in
testing eligibility in Ontario on December 31st, 2021.
28
By
examining these factors, the study aims to enhance the
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understanding of the dynamics influencing the quality of WEM-
clinical metrics correlations and to contribute to a more eective
integration of WEM in public health surveillance, thereby
enhancing its actionability in future disease monitoring eorts.
2. METHODOLOGY
2.1. Epidemiological WEM and Clinical Data. The
Ontario-wide epidemiological information, including WEM
data, daily hospital admissions, and daily laboratory-positive
cases, was collected and shared internally from the Ontario WSI
Data and Visualization Hub. This Hub, operated by the
Government of Ontario, was accessible only to municipalities,
public health units, and academic and research institutions
participating in the Ontario WSI program through the Ontario
Wastewater Surveillance Consortium (OWSC). The WEM data
generated from the Ontario WSI program, covering the period
from January first, 2021 to March 31st, 2023, is described in
further detail in ref. no. 18, is available for download in CSV
format from Zenodo
42
and GitHub repository (https://github.
com/OntarioWastewaterSurveillanceConsortium/sars-cov-2-
data).
The WEM data used in this study consisted of SARS-CoV-2
N1 and N2 genomic copies, both as concentration, non-
normalized (in cp/L of sample collected) and normalized by the
biomarker PMMoV (in cp/cp of PMMoV) for each WEM site
within the Ontario WSI program from January first, 2021, to
March 31st, 2023. Wastewater samples were collected and
transported on ice for all sites under the Ontario WSI program,
where SARS-CoV-2 RNA enrichment and extraction were
performed within 48−72 h ref. no. 18. Most participating
institutions enriched the solids fraction of wastewater prior to
nucleic acid extraction of the SARS-CoV-2 and PMMoV targets
ref. no. 18. Extracted nucleic acids were stored under controlled
conditions and analyzed through RT-qPCR within 72 h of
sample enrichment ref. no. 18. All participating testing
institutions analyzed for two gene regions of the SARS-CoV-2
genome, particularly the N1 and N2 gene regions, as well as the
PMMoV biomarker for normalization of the SARS-CoV-2
genome ref. no. 18. Detailed methods, including sample
collection, enrichment, concentration, extraction, and RT-
qPCR quantification of SARS-CoV-2 and PMMoV employed
by each laboratory are extensively described by ref. no. 18
Hospital admissions data acquired in this study from the Hub
(internally accessible to participating institutions under the
Ontario WSI program) specifically pertains to patients whose
primary diagnosis was COVID-19 (patients admitted to the
hospital primarily due to COVID-19 related symptoms or
complications). Laboratory-positive case data acquired from the
Hub are based on the date reported (i.e., case by reported date).
These clinical surveillance metric data sets (hospital admissions
and laboratory-positive cases) were geospatially matched with
the WEM data set by Ontario’s Ministry of the Environment,
Conservation and Parks (MECP). This process involved linking
patients’ postal codes from the clinical data sets to the
corresponding WES sewershed catchments, ensuring that the
data sets reflect the same population. While sewershed
boundaries are not identical to clinical reporting regions, this
method allowed for alignment between the data sets. This
ensured that the analyses of the WEM data and the clinical
surveillance metrics reflect the actual impact of COVID-19
within the same populations. The population size of each
surveyed sewershed was also acquired from the Hub.
Vaccination information for the province of Ontario was
obtained from Public Health Ontario’s Respiratory Virus Tool
for each study location.
43
Figure 1. Geographic overview of Ontario and the study locations. Blue points represent locations analyzed during both the Omicron BA.1 and BA.2
waves, while black triangles indicate locations analyzed only during the Omicron BA.1 wave. WEM, hospital admissions, and laboratory-positive case
data were collected at these locations with all data sets linked to the population of the respective surveyed wastewater sewershed, ensuring a
comprehensive representation of the interconnected epidemiological metrics across the same geospatial locations.
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2.2. Geospatial Locations. This study collected and
compared epidemiological data sets, including WEM data,
hospital admission data, and laboratory-positive case data, for
the Omicron BA.1 and BA.2 waves at specific geospatial
locations across Ontario. Out of the 107 WEM sites surveyed
under the program at its peak, data from a total of 58 sites were
investigated during the Omicron BA.1 wave (Figure 1), and
from 50 sites were investigated during the Omicron BA.2 wave
(Figure 1), following an exclusion process using criteria detailed
in Section 2.2.1.
2.2.1. Exclusion of Locations Prior to Analysis. Prior to the
analysis of the Ontario WSI data set, several criteria were
established to ensure the robustness and reliability of the
findings. These considerations aimed to filter out sites and data
segments that might introduce bias or limit the statistical power
of the analysis.
2.2.1.1. Minimal Clinical Surveillance Metrics. To ensure
robust statistical analyses of the study’s three epidemiological
data sets, this research included only study locations with
significant population-COVID-19 activity. Specifically, locations
were excluded if they reported no more than one hospital
admission or had fewer than 3 days with daily admissions
exceeding one hospital admission during the Omicron BA.1 or
BA.2 waves. Although this specific criterium has not been
previously discussed in the literature, its implementation was
necessary to address the frequent discrete values in the hospital
admissions, a factor that has been found to underestimate the
true correlation between variables, in this case WEM data and
clinical surveillance data.
44,45
These exclusions are intended to
ensure sucient data quality for meaningful WEM-clinical
surveillance correlation analyses. This resulted in the exclusion
of up to 57 (out of 107) sites with populations ranging from 279
to 47,868 inhabitants for the WEM-hospital admission
correlation analyses of both Omicron BA.1 and BA.2 waves,
maintaining the clinical relevance of the analysis by reflecting
substantial COVID-19 transmission. Specifically, for the
Omicron BA.1 wave, 49 sites were excluded, leaving 58 sites
in the WEM-hospital admission correlation analysis. For the
Omicron BA.2 wave, 57 sites were excluded under these criteria,
resulting in 50 sites for WEM-hospital admission analysis. In
addition, study locations were excluded from the data set used in
this study if no more than one laboratory-positive case was
reported during the BA.1 or BA.2 wave. One site met this
exclusion criterion but was already excluded by the hospital
admission criteria during the Omicron BA.1 and BA.2 waves.
2.2.1.2. Low Wastewater Sampling Frequency. Adequate
wastewater sampling frequency is crucial for capturing
fluctuations in WEM data and ensuring robust statistical
power when determining Spearman’s rank correlation coef-
ficient (ρ) between the respective WEM data and hospital
admissions across Ontario. Thus, study locations where less than
three samples per week were analyzed were excluded from this
analysis.
46
Eighteen sites (279 to 47,868 inhabitants) were
identified with this criterion; however, those sites were
previously excluded from the analysis by the hospital admissions
criteria outlined in Section 2.2.1.1.
2.3. Analysis Period of Omicron BA.1 and Omicron
BA.2 Waves across the Study Locations. The onset of the
Omicron BA.1 and Omicron BA.2 waves were estimated from
the WEM data set for each respective Ontario WSI site due to
limitation in obtaining population-wide VOC sequencing data
for each individual site. The analysis period for the Omicron
BA.1 and BA.2 waves was set to 80 days (∼11 weeks),
47
encompassing 40 days before and 40 days after the identified
peaks for each WEM site. To account for potential asynchronous
spread of the virus across the province, the peaks were identified
through visualization of the WEM data and the clinical
surveillance data for each individual WEM site.
2.4. Data Analysis. 2.4.1. Spearman’s Coecient between
WEM Data and Clinical Metrics. Spearman’s rank correlation
coecient (ρ) was calculated for all the Ontario WSI sites
between the 7-day midpoint average of WEM data, both non-
normalized (in cp/L) and normalized (in cp/cp), and the 7-day
midpoint average of hospital admissions, and the 7-day midpoint
average of laboratory-positive cases. The data sets were analyzed
during the Omicron BA.1 and Omicron BA.2 waves. The
forward time step (lead time of the epidemiological metric) of
1−14 days with the strongest correlation between the data sets,
along with the visual alignment in recorded peaks, was
considered when selecting the optimal lead time between
wastewater measurements and hospital admission data,
providing the “maximum Spearman’s coecient (ρ)” used
throughout this study.
24
A value of p< 0.05 was used to indicate
a statistically significant correlation. As the analyses were
conducted independently for each site to characterize localized
relationships, no corrections for multiple comparisons were
applied. The Spearman’s rank correlation analysis was chosen
because of the absence of linearity in hospital admission data,
WEM data, and laboratory-positive cases.
48
The analysis was
performed in RStudio and accounts for ties in rankings by
assigning average ranks to tied values during the calculation
process.
Throughout this study, the maximum Spearman’s coecient
(ρ) within the 1−14-day time step was used as an index to define
correlation quality at the Ontario WSI sites. Correlations were
evaluated based on their magnitude rather than their direction,
with both strong positive and strong negative correlations
suggesting good correlation quality between two metrics.
Conversely, a weak or near zero Spearman’s coecient (ρ)
might indicate discrepancies in the rankings,
48,49
suggesting
potential problems with data consistency and accuracy or
reporting limitations in either the WEM data or clinical
surveillance metrics.
2.4.2. Variability of WEM Data and Clinical Metrics. To
provide a measure of fluctuation/variability in WEM data and
clinical surveillance metrics, specifically hospital admissions and
laboratory-positive cases, and to quantitatively assess whether it
aects the WEM-clinical metric correlation quality of the
surveillance data, the variability of both parameters was
calculated longitudinally for each study location using eq 1.
The mean variability across the range of the Omicron BA.1 and
BA.2 VOCs for each site was then calculated.
n
x
variability 17 day midpoint avg.
7 day midpoint avg.
i
n
i
1
=
| |
=
(1)
Where:
•xiis the daily data point for WEM signal or clinical
surveillance metrics.
•7-day midpoint avg. is the moving average of the 7-day
window centered around xifor each date.
•nis the total number of data points for the given WEM
site.
2.4.3. Recursive Partitioning and Regression Trees. To
explore the relationships between the factors possibly
influencing the epidemiological metric correlations, regression
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trees were constructed. Explanatory variables included pop-
ulation size of the surveyed sewersheds, range in the 7-day
average hospital admissions, proportion of zeros in the clinical
metrics data, and isolation status of the sampling site (isolated vs
not isolated). In this study, isolated study locations are described
as those at least 350 km away from a major city center that oers
essential government services and is accessible by roads
throughout the year.
50
The range in the 7-day midpoint average
hospital admissions reflects the occurrence of unique values in
the daily hospital admissions over a week. This is important
because fewer unique values (i.e., many similar daily hospital
admissions) can result in similar 7-day midpoint averages,
leading to ties in the Spearman’s rank correlation between
WEM-clinical surveillance metric correlations. The proportion
of zeros in the clinical metrics data was calculated by
determining the percentage of days with zero reported hospital
admissions or laboratory-positive cases over the duration of the
studied VOC. The R statistical environment (version 4.3.3) was
used for the analysis with the “rpart” and “rpart.plot” packages
supporting tree creation and visualization.
51
3. RESULTS AND DISCUSSION
3.1. Large Range of Correlations and Lead Time
between WEM and Clinical Metrics across Ontario,
Canada. A large range of correlations was observed between
the 7-day midpoint average wastewater viral signal (cp/L and
cp/cp) and both 7-day midpoint average hospital admissions
and 7-day midpoint average laboratory-positive cases during the
Omicron BA.1 and BA.2 waves (Figure 2). For hospital
admissions, the Spearman’s rank correlation coecient (ρ)
between WEM data and hospital admissions ranged from
nonexistent (ρ=−0.188 in cp/L and ρ= 0.026 in cp/cp) to very
strong (ρ= 0.859 in cp/L and ρ= 0.955 in cp/cp) across both
the Omicron BA.1 and BA.2 waves (Figure 2A,B,E,F). Similarly,
for laboratory-positive cases, ρranged from nonexistent (ρ=
−0.261 in cp/L and ρ= 0.026 in cp/cp) to very strong (ρ=
0.970 in cp/L and ρ= 0.971 in cp/cp) (Figure 2C,D,G,H).
While the analytical methods used in the Ontario WSI data set
were largely based on or similar to the analytical protocols of ref.
no. 52, it is noted that across the 13 institutions, a variety of
specific and distinct steps and protocols were used by
laboratories for sample collection, viral RNA concentration,
extraction and RT-qPCR quantification of SARS-CoV-2 and
PMMoV, which may introduce confounding eects in this
analysis. Since diering analytical methods were used to compile
the Ontario WSI data set, the potential confounding eect of
various methods on the quality of WEM-clinical metrics
correlations was first determined in this study. This range in
correlation was found to not vary based on the specific methods
used by the 13 participating academic institutions under the
Figure 2. Heatmap illustrating the range in correlations between wastewater signals (WEM data) and clinical metrics (hospital admissions and
laboratory-positive cases data) during the Omicron BA.1 and BA.2 waves. (A, E) Correlations between WEM (in cp/L) and hospital admissions, (B, F)
correlations between WEM (in cp/cp) and hospital admissions. (C, E) Correlations between WEM (in cp/L) and laboratory-positive cases, and (D,
H) correlations between WEM data (in cp/cp) and laboratory-positive cases. The x-axis represents the lead time (0−14 days), and the y-axis
represents the wastewater surveillance study locations, ordered from weakest to strongest average Spearman’s Correlation (ρ). Negative and
nonsignificant correlations (p> 0.05) are shown as blank areas. Color intensity indicates the strength of correlation, with purple denoting very weak or
nonexistent correlation, and yellow indicating very strong correlation. Statistically nonsignificant correlations (ρnear 0.00 and p> 0.05) are denoted in
purple.
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Ontario WSI program, and thus was not included as a possible
confounder for WEM and clinical surveillance correlations in
this study.
3.2. Eect of Population Size on WEM and Clinical
Metrics. The relationships between the population size of the
surveyed sewersheds (the 58 sites during the Omicron BA.1
wave and the 50 sites during the Omicron BA.2 wave) and the
three major clinical indicators of COVID-19: WEM, hospital
admissions, and laboratory-positive cases were first evaluated.
Specifically, the cumulative WEM concentration, non-normal-
ized WEM viral signal (cp/L), normalized WEM viral signal
(cp/cp) and the cumulative clinical surveillance metrics for each
of the sites were assessed.
Population size of the surveyed sewersheds considered in this
study following the exclusion criteria detailed in Section 2.2.1 (n
= 58 during the Omicron BA.1 wave, and n= 50 during the
Omicron BA.2 wave) and the cumulative non-normalized WEM
signal (cp/L) and normalized WEM signal (cp/cp) per each
WEM surveyed site displayed no significant correlation during
both the Omicron BA.1 wave (in cp/L: ρ= 0.1145, p= 0.3920
and in cp/cp: ρ=−0.2547, p= 0.0537, respectively) (Figure
3A,B) and the Omicron BA.2 wave (in cp/L: ρ= 0.1109, p=
Figure 3. Relation between population size of the surveyed sewershed (x-axis) and cumulative, concentration non-normalized WEM data (cp/L) (A,
E), cumulative normalized WEM data (cp/cp) (B, F), cumulative hospital admissions (C, G), and cumulative laboratory-positive cases (D, H) against
population size of the surveyed wastewater sewershed during the Omicron BA.1 (top row) and Omicron BA.2 (bottom row) waves. ρexclusively refers
to Spearman’s rank correlation coecient. *Laboratory-positive cases in all analyzed sites are underreported during the peak of the Omicron BA.1
wave and throughout the Omicron BA.2 wave due to updated PCR eligibility in Ontario as of December 31st, 2021.
Figure 4. Relation between population size of the surveyed sewersheds (x-axis) and WEM data variability (y-axis) (A, D), hospital admissions
variability (y-axis) (B, E), and laboratory-positive case variability (y-axis) (C, F) the Omicron BA.1 (top row) and BA.2 (bottom row) waves.
*Laboratory-positive cases in all analyzed sites are underreported halfway during the Omicron BA.2 wave and throughout the Omicron BA.2 wave due
to updated PCR eligibility in Ontario as of December 31st, 2021.
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0.4433 and in cp/cp: ρ=−0.2190, p= 0.1265, respectively)
(Figure 3E,F). At population sizes below 300,000 inhabitants
which represented the majority of sites (50/58 during Omicron
BA.1 and 42/50 during Omicron BA.2), cumulative WEM signal
trends (in cp/L and cp/cp) remain consistent (Figure
3A,B,E,F). The slight elevation observed with the normalized
WEM (cp/cp) in sites with populations below 300,000 is more
likely attributed to the dierences in the quantification of the
PMMoV normalization biomarker among the 13 participating
academic institutions within the Ontario WSI program (Figure
3B,F). Population size and the cumulative hospital admissions
displayed a strong and significant correlation, during both the
Omicron BA.1 wave (ρ= 0.8448, p< 0.05) (Figure 3C) and the
Omicron BA.2 wave (ρ= 0.7915, p< 0.05) (Figure 3G).
Population size of the surveyed sewersheds and the cumulative
laboratory-positive cases displayed a similar observation during
both the Omicron BA.1 wave (ρ= 0.9662, p< 0.05) (Figure 3D)
and the Omicron BA.2 wave (ρ= 0.8795, p< 0.05) (Figure 3H).
These outcomes align with the expectation that WEM data
diers fundamentally from hospital admissions and laboratory-
positive cases in its measurement approach. Within the context
of this study and the viral quantification methods used by the
Ontario WSI program, the WEM data is a measurement
performed on a representative volume or mass of wastewater
that may be further normalized by the mass of fecal material
within the wastewater, inherently reducing its dependence on
population size of the surveyed sewersheds and thus may be
further capable of providing clinical insights into transmission
dynamics in smaller population sizes (Figures S1 and S2 in
Supporting Information). In contrast, COVID-19 caused
hospital admission and laboratory-positive cases are measured
as discrete counts of illness measures within a population.
Consequently, larger populations inherently experience higher
occurrences of COVID-19-caused hospital admission and
higher incidence of laboratory-positive cases, reflecting both
the larger number of individuals at risk and the concentration of
healthcare resources in urban centers. In addition to the location
population, these healthcare facilities may also serve patients
from smaller communities.
3.3. Eect of Population Size on Variability of WEM
and Clinical Metrics. The relationship between the population
size of the surveyed sewersheds and variability of WEM data,
hospital admissions, and laboratory-positive cases were
investigated in this study during the Omicron BA.1 and BA.2
waves. Variability in WEM data (non-normalized and
normalized) exhibited a moderate and significant negative
correlation with population size during the Omicron BA.1 wave
(in cp/L: ρ=−0.3901; p< 0.05, and in cp/cp: ρ=−0.4534; p<
0.05) (Figure 4A). During the Omicron BA.2 wave, a weak to
moderate negative correlation was observed between the WEM
data variability and population size of the surveyed sewershed
(in cp/L: ρ=−0.3781; p< 0.05, and in cp/cp: ρ=−0.2726; p=
0.055) (Figure 4D). At population sizes below 300,000
inhabitants which represented the majority of sites (50/58
during Omicron BA.1 and 42/50 during Omicron BA.2),
cumulative WEM signal trends (in cp/L and cp/cp) remain
consistent (Figure 4A,D). A strong and significant negative
correlation was observed between the variability of hospital
admissions and population size during both the Omicron BA.1
wave (ρ=−0.8384; p< 0.05) (Figure 4B) and the Omicron
BA.2 wave (ρ=−0.7516; p< 0.05) (Figure 4E). A strong and
significant negative correlation was similarly observed between
the variability of laboratory-positive cases and population size
during the Omicron BA.1 wave (ρ=−0.8212; p< 0.05) with a
mean variability of 0.423 ±0.152 (Figure 4C) and the Omicron
BA.2 wave (ρ=−0.8086; p< 0.05) (Figure 4F). Visual
observation indicates that both hospital admissions and
laboratory-positive cases exhibit higher variability in smaller
populations, as indicated by the higher data points at lower
populations in Figure 4B−F. This higher variability in hospital
admissions and laboratory-positive cases at lower populations
decreases significantly as population size increases. In contrast,
the variability of WEM data across dierent population sizes is
shown to be more consistent across all population sizes (Figure
4).
WEM data exhibited lower mean variability compared to
hospital admissions and laboratory-positive cases. During the
Omicron BA.1 wave, the mean variability of WEM data was
0.399 ±0.086 (in cp/L) and 0.408 ±0.078 (in cp/cp). During
the Omicron BA.2 wave, the mean variability of the WEM data
was 0.408 ±0.098 (in cp/L) and 0.404 ±0.077 (in cp/cp). As
for hospital admissions, the mean variability was 1.027 ±0.301
during the Omicron BA.1 wave, and 1.048 ±0.321 during the
Omicron BA.2 wave, indicating significant fluctuations in daily
hospital admissions, exceeding the 7-day moving average by
more than 100%. This level of fluctuation is particularly
prevalent at study locations with limited range of discrete
hospital admission counts, often between 0 and 3 daily
admissions, and at locations with zero-heavy count data for
hospital admissions (proportion of zero hospital admission
exceeding 0.50 in 39 out of the 58 sites during the Omicron BA.1
wave, and in 33 out of the 50 sites during the Omicron BA.2
wave) (Figures S1 and S2, respectively, in Supporting
Information). The significant variability in hospital admissions
reported by regions with smaller populations is attributed to the
narrow daily count range (e.g., 0, 1, or 2 admissions), which
results in a larger relative dierence between the daily hospital
admission counts and the 7-day average. This is in contrast to
the lower variability (smaller relative dierences from the 7-day
average) measured in larger populations, despite the wider range
of data points, consistent with the premise that larger
populations with a higher frequency of events produce more
consistent hospital admissions data. As for laboratory-positive
cases, the mean variability is 0.423 ±0.152 during the BA.1 wave
and 0.369 ±0.126 during the BA.2 wave. Thus, while WEM data
do demonstrate some variability with population size, which is
likely attributed to higher sensitivity to individual shedding
rates,
53
WEM data are markedly more stable across dierent
population sizes compared to hospital admissions and
laboratory-positive cases. The eect of the variability of the
WEM data (in cp/L and cp/cp), hospital admissions, and
laboratory-positive cases on the strength of the maximum
Spearman’s coecient (ρ) between WEM-hospital admission
and WEM-laboratory-positive cases during the Omicron BA.1
and BA.2 waves are discussed in more detail in Sections S1 and
S2 of the Supporting Information.
3.4. Driving Factors of WEM Correlation with Clinical
Metrics. 3.4.1. Driving Factors during Period of Waned
Vaccination Immunization in Ontario. The Omicron BA.1
wave exhibited a significant surge in all three epidemiological
metrics: WEM data, hospital admissions, and laboratory-
positive cases across all the sites analyzed in this study (Figure
S2 in Supporting Information). This surge is likely attributed to
a combination of factors, including the waning of vaccine
eectiveness against symptomatic disease caused by highly
infectious Omicron BA.1 VOC, its vaccine escape proper-
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ties,
24,54
and its emergence during the winter months
(November 2021 to March 2022), during which rise in
symptomatic illness from respiratory viruses, including SARS-
CoV-2, were previously observed.
55,56
At the onset of the
Omicron BA.1 wave across Ontario, approximately 10 weeks
had passed since 70% of Ontario’s total population had achieved
full vaccination immunization (two doses of COVID-19
vaccine) by October third, 2021,
43
thus subjecting the province
to waned vaccine immunity.
24
In the analysis of the factors
driving the quality of the maximum WEM-hospital admissions
Spearman’s coecient (ρ), and the maximum WEM-laboratory-
positive cases Spearman’s coecient (ρ) during this period of
the COVID-19 pandemic, a regression tree model was applied
(detailed methodology is provided in Section 2.4.3). This model
explored the following parameters hypothesized to contribute to
undermining the Spearman’s coecient (ρ): population size of
the surveyed sewersheds, 7-day average hospital admissions
range, 7-day average laboratory-positive case range, the
proportion of zero admissions, the proportion of zero reported
case count, and site isolation status (Figure 5). The regression
tree analysis, stratified by the two Omicron subvariants, revealed
distinct population thresholds at which the maximum WEM-
hospitalization Spearman’s coecient (ρ) and the WEM-
laboratory-positive case Spearman’s coecient (ρ) quality
diverge.
During this period of waned vaccination immunization, the
regression tree analysis suggested an approximate population
threshold of 66,000 inhabitants (Figure 5A). Beyond this
threshold, there is a 90% probability of resulting in a strong
WEM-hospital admissions Spearman’s coecient (ρ) (ρ>
0.650) indicating a consistent relationship between WEM data
and hospital admissions (Figure 5A). This threshold can be
further visually illustrated by the moderate to strong and
significant relation observed between the population size of the
surveyed sewersheds and the maximum Spearman’s coecient
(ρ) of WEM-hospital admissions (in cp/L: ρ= 0.6676, p< 0.05
and in cp/cp: ρ= 0.6132, p< 0.05, respectively) (Figure 5B).
For surveyed sewershed with population sizes below or equal to
66,000 inhabitants, the limited range in the 7-day average
hospital admissions data (fewer than 3 or 4 daily values) likely
contributed to undermining the WEM-hospital admissions
Spearman’s coecient (ρ), with a range lower than six unique 7-
day average hospital admissions exhibited a 1.00 (100%)
probability of resulting in a “poor” “Spearman’s coecient (ρ)
(ρ< 0.650) (Figure 5A). This is consistent with previous
observations that limited daily occurrence of clinical surveillance
metrics resulted in a poor Spearman’s coecient with WEM
data.
57
Site isolation status of the Ontario WSI sites was found
not to drive the maximum WEM-hospital admissions Spear-
man’s coecient (ρ).
Similarly, the regression tree analysis identified a population
threshold of approximately 68,000 for WEM-laboratory-positive
case correlations, with a 97% probability of strong correlations
(ρ> 0.750) in populations exceeding this threshold (Figure 5C).
This threshold can be further visually illustrated where a weak to
moderate relation existed between the population size of the
Figure 5. Regression trees representing classification models for evaluating the maximum WEM-clinical metric quality during the Omicron BA.1
wave−coinciding with a period of waned vaccination immunity: (A, C) regression trees for WEM-hospital admissions and WEM-laboratory-positive
cases; (B) relation between maximum WEM-hospital admission Spearman’s coecient (ρ) and population size; (D) relation between maximum
WEM-laboratory-positive case Spearman’s coecient (ρ) and population size. Each node shows the predicted class (“Good” WEM-hospital
admissions and WEM-laboratory-positive cases correlation, ρ> 0.650 or “Poor” WEM-hospital admissions and WEM-laboratory-positive cases
correlation, ρ< 0.650), the predicted probability (between 0% and 100%) of a “Poor” outcome, and the percent of observations in the node. *“Good”
laboratory-positive cases-WEM correlation classified as ρ> 0.750 due to insucient observations under “Poor” outcome classification with ρ< 0.650.
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surveyed sewersheds and the maximum WEM-laboratory-
positive case correlation (in cp/L: ρ= 0.3159, p< 0.05 and in
cp/cp: ρ= 0.5029, p< 0.05) (Figure 5D). The poorer WEM-
laboratory-positive case correlations at populations under
68,000 are likely attributed to the policy change of population-
wide PCR eligibility in Ontario on December 31st, 2021,
halfway through the Omicron BA.1 wave, which consequently
resulted in underreporting of laboratory-positive cases and lack
of true representation of disease incidence.
28
This policy driven
underreporting aected the resolution of laboratory-positive
case data but did not influence the quality of WEM data.
27,58
As
the population size of the surveyed sewersheds decreases, the
implication of these changes on the relationship between WEM
data and laboratory-positive cases becomes more apparent.
WEM-laboratory-positive case correlations were generally
stronger (mean ρ: 0.821 ±0.138) compared to WEM-hospital
admissions correlations (mean ρ: 0.682 ±0.194), which is likely
due to the greater occurrence of daily laboratory-positive cases
and lower proportion of zeros (3.0% ±7.0% compared to 57.2%
±22.7% with hospital admissions data). Despite this, a
population size threshold of approximately 68,000 inhabitants
was again identified. This indicates that for WEM sites servicing
populations below this threshold, WEM could potentially
provide a better understanding of disease burden. It is important
to note that these thresholds are not hard boundaries but instead
reflect trends within the context of this specific data set. These
thresholds are best understood as guides to understanding
relationships between population size, WEM data, and clinical
surveillance metrics, under the conditions of limited vaccination
immunity during the Omicron BA.1 wave.
3.4.2. Driving Factors during Period of Significant
Vaccination Immunization in Ontario. The Omicron BA.2
wave coincided with a period of significant vaccination
immunization following a significant rise in booster dose
administration across Ontario.
24,43
During this period, the
regression tree analysis identified an approximate population
threshold of 187,000 inhabitants. Areas exceeding this threshold
had a high probability (87%) to exhibit a strong Spearman’s
coecient (ρ) (ρ> 0.650) (Figure 6A). This threshold is further
visually illustrated by the moderate to strong and significant
relationships between the population size of the surveyed
sewersheds and the maximum Spearman’s coecient (ρ)
between WEM data (in cp/L and cp/cp) and hospital
admissions during the Omicron BA.2 wave (ρ= 0.5649, p<
0.05 and ρ= 0.4704, p< 0.05) (Figure 6B). When the
population size of the surveyed sewershed is below or equal to
187,000 inhabitants, the limited range in hospital admissions
data further contributed to undermining the WEM-hospital
admissions Spearman’s coecient (ρ), with a 7-day average
hospital admissions range below eight admissions having a 100%
predicted probability of poor correlation between WEM and
hospital admissions (ρ< 0.650) (Figure 6A). Additionally, for
Figure 6. Regression trees representing classification models for evaluating the maximum WEM-clinical surveillance quality during the Omicron BA.2
wave−coinciding with a period of significant vaccination immunity: (A, C) regression tress for WEM-hospital admissions and WEM-laboratory-
positive cases; (B) relation between maximum WEM-hospital admission Spearman’s coecient (ρ) and population size (D) relation between
maximum WEM-laboratory-positive case Spearman’s coecient (ρ) and population size. Each node shows the predicted class (“Good” WEM-hospital
admissions and WEM-laboratory-positive cases correlation, ρ> 0.650 or “Poor” WEM-hospital admissions and WEM-laboratory-positive cases
correlation, ρ< 0.650), the predicted probability (between 0 and 100%) of a “Poor” outcome, and the percent of observations in the node.
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sites with greater hospital admission ranges, a high proportion of
zeros in hospital admissions greater than 0.55 had a 100%
predicted probability of a weaker WEM-hospital admissions
Spearman’s coecient (ρ) (ρ< 0.650) (Figure 6A). Site
isolation status of the Ontario WSI sites was found not to drive
the maximum WEM-hospital admissions Spearman’s coecient
(ρ).
For the WEM-laboratory-positive case Spearman’s coecient
(ρ), the regression tree analysis suggested an approximate
population threshold of 238,000 was identified, above which
there was a 92% probability of strong correlations (ρ> 0.650)
(Figure 6C). This threshold can be visually illustrated where a
weak to moderate relation existed between the population size of
the surveyed sewersheds and the maximum Spearman’s
coecient (ρ) between WEM data (in cp/L and cp/cp) and
laboratory-positive cases during the Omicron BA.2 wave (ρ=
0.4211, p< 0.05 and ρ= 0.2554, p= 0.0735, respectively)
(Figure 6D). In populations below this threshold, the relation-
ship between WEM and laboratory-positive cases were weaker
(ρ> 0.650), likely attributed to the reduced reporting accuracy
due to the policy changes of population-wide PCR eligibility,
28
and reduced incidence of symptomatic disease due to significant
vaccination immunization. This suggests that the maximum
obtainable association between WEM and clinical metrics is
influenced by population size, data variability, and the
completeness of clinical surveillance metrics.
Dierences in the population thresholds of 66,000−68,000
during a period of waned vaccination immunization (BA.1) and
187,000−238,000 a period of significant vaccination immuniza-
tion (BA.2) wave are likely attributed to the dierences in the
population immunity dynamics between the two waves, which
influenced the relation between WEM data, hospital admissions,
and laboratory-positive cases.
24
The rapid increase in booster
dose administration (third mRNA COVID-19 vaccine) across
Ontario between January 2022 and February 2022, combined
with natural immunity among those who acquired COVID-19
during the BA.1 wave, the Omicron BA.2 wave displayed an
overall significant decrease (18.90% ±39.90) in the cumulative
hospital admissions and cumulative laboratory-positive case
compared to the BA.1 wave. Booster vaccination, which
provides up to 90% protection against severe or fatal disease
from the Omicron BA.2 subvariant at 7 weeks postvaccination,
59
were administered to over 45% of Ontario’s population by
March 13th, 2022.
43
Additionally, widespread transmission of
the Omicron BA.1 VOC contributed to further natural
immunity, as evidenced by the rise in anti-N antibodies
(indicative of infection) from a seroprevalence of 4.5% on
December seventh, 2021 to 27.8% on March 15th, 2022.
60
These high level of vaccination coverage, combined with the
increase in anti-N antibodies prior to the peak of the Omicron
BA.2 wave in mid-March 2022, likely contributed to a decrease
in hospital admissions (Figure 3G) and laboratory-positive cases
(Figure 3H) compared to the BA.1 wave. This reduced severity
of COVID-19-caused symptoms undermined the WEM-
hospital admissions Spearman’s coecient (ρ) during this
period, particularly in populations below 187,000 and 238,000.
As such, a rank-based method may be untenable in the
interpretation of the relationship between WEM data, hospital
admissions, and laboratory-positive cases under certain
conditions. Despite this reduced severity, WEM was demon-
strated to more accurately reflect the viral circulation within a
community.
57
WEM data, hospital admissions, and laboratory-
positive cases are all reliable indicators of COVID-19 disease
burden when applied to populations above approximately
238,000 inhabitants, during a period of significant vaccination
immunization, while WEM data might be the only reliable
indicator of disease burden in smaller populations. It is
important to note that the population thresholds identified by
the regression tree analysis are not definitive boundaries but
instead reflect trends within the context of this data set. They
highlight the general relationship between population size,
WEM data, and clinical surveillance metrics, particularly under
the conditions of significant vaccination immunization during
the Omicron BA.2 wave.
4. CONCLUSIONS AND RECOMMENDATIONS
This study represents the first comprehensive, province-wide
investigation of geospatially linked wastewater viral signal levels
and their association with hospital admissions and laboratory-
positive cases during two distinct stages of the COVID-19
pandemic. It was found that population size significantly
influenced the quality of correlations between WEM data and
clinical surveillance metrics, specifically hospital admissions and
laboratory-positive cases, during the Omicron BA.1 and BA.2
waves. WEM data was more stable across dierent population
sizes than clinical surveillance metrics at both waves. Smaller
populations exhibited higher variability in hospital admissions
and laboratory-positive cases, contributing to poorer correlation
quality with WEM data. During the Omicron BA.1 wave, study
location populations with over 66,000 inhabitants had a higher
probability (90%) of achieving strong correlation between
WEM and hospital admissions (ρ> 0.650). Similarly, a
threshold of approximately 68,000 inhabitants indicated a high
probability (97%) of strong correlations (ρ> 0.750) for
laboratory-positive cases. For the Omicron BA.2 wave, these
thresholds increased to approximately 187,000 for hospital
admissions and 238,000 for laboratory-positive cases, reflecting
the enhanced vaccine coverage and natural immunity. Below
these thresholds, when clinical surveillance metrics are limited,
WEM can be a reliable tool for reflecting viral circulation in
smaller communities. Inconsistent lead times were not found to
be driven by population size and are more likely attributed to
complex disease transmission dynamics and clinical data
reporting schedules within each community.
The results underscore a pivotal distinction: as population size
increases, the relation between hospital admissions, laboratory-
positive cases, and wastewater data becomes more pronounced
and consistent. Simultaneously, the population size of the
surveyed sewershed demonstrated no influence on the WEM
data across the Ontario WSI. The bifurcation of the population
into two categories�those with no more than one hospital
admission and those where incidence trends are more
discernible through wastewater data�becomes particularly
apparent at regions servicing lower population areas. With 49
sites (during Omicron BA.1) and 57 sites (during Omicron
BA.2) in the Ontario WSI program lacking sucient clinical
surveillance data, the ability of wastewater surveillance to
measure disease burden eciently and eectively is highlighted
especially in smaller populations, regardless of the current state
of population immunity.
A limitation of this study is that hospital admission data
included in this analysis specifically pertains only to patients with
a primary diagnosis of COVID-19. Expanding this to include
secondary and tertiary diagnoses, as well as considering
emergency department visits with a COVID-19 diagnosis,
could potentially improve Spearman’s correlation (ρ) by
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increasing the incidence of nonzero hospitalization days in
smaller regions, and provide a more comprehensive under-
standing of disease health burden across the study locations.
Additionally, dierences in sewerage systems (e.g., network
length, type of sewer system�mixed or separate channels) or
physicochemical properties could potentially influence the
wastewater signals and introduce variability in the WEM data.
Further studies should consider these factors to refine and
interpret WEM as a complementary surveillance metric.
■ASSOCIATED CONTENT
*
sı Supporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acsestwater.4c00958.
Details on the time series relationship between WEM data
and hospital admissions from November 2021 to June
2022, covering the Omicron BA.1 and BA.2 waves for
each site considered in this study (the 58 sites during the
Omicron BA.1 wave and 50 sites during the Omicron
BA.2 wave). Further analysis examining the impact of
WEM and clinical surveillance data (both hospital
admissions and laboratory-positive cases) variability on
correlation quality, including Spearman’s correlation
coecients and statistical significance testing, assessing
how fluctuations in reported laboratory-positive cases
influence WEM-laboratory-positive case correlations
during both Omicron waves. Details on the relationship
between population size and lead times between WEM
data and clinical surveillance data (both hospital
admissions and laboratory-positive cases), presenting
statistical analysis and visualization (PDF)
■AUTHOR INFORMATION
Corresponding Author
Robert Delatolla −Department of Civil Engineering, University
of Ottawa, Ottawa, Ontario K1N 6N5, Canada;
Email: Robert.Delatolla@uottawa.ca
Authors
Nada Hegazy −Department of Civil Engineering, University of
Ottawa, Ottawa, Ontario K1N 6N5, Canada; orcid.org/
0000-0003-4277-076X
K. Ken Peng −Department of Statistics and Actuarial Science,
Simon Fraser University, Burnaby, British Columbia V6T
1Z4, Canada
Patrick M. D’Aoust −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Lakshmi Pisharody −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Elisabeth Mercier −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Nathan Thomas Ramsay −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Md Pervez Kabir −Department of Civil Engineering, University
of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Tram Bich Nguyen −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Emma Tomalty −Department of Civil Engineering, University
of Ottawa, Ottawa, Ontario K1N 6N5, Canada;
orcid.org/0009-0007-4956-5546
Felix Addo −Department of Civil Engineering, University of
Ottawa, Ottawa, Ontario K1N 6N5, Canada
Chandler Hayying Wong −Department of Civil Engineering,
University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Shen Wan −Department of Civil Engineering, University of
Ottawa, Ottawa, Ontario K1N 6N5, Canada
Joan Hu −Department of Statistics and Actuarial Science,
Simon Fraser University, Burnaby, British Columbia V6T
1Z4, Canada
Charmaine Dean −Department of Statistics and Actuarial
Science, University of Waterloo, Waterloo, Ontario N2L 3G1,
Canada
Minqing Ivy Yang −BioZone, Department of Chemical
Engineering and Applied Chemistry, University of Toronto,
Toronto, Ontario M5S 3ES, Canada
Hadi Dhiyebi −Department of Biology, University of Waterloo,
Waterloo, Ontario N2L 3G1, Canada
Elizabeth A. Edwards −BioZone, Department of Chemical
Engineering and Applied Chemistry, University of Toronto,
Toronto, Ontario M5S 3ES, Canada; orcid.org/0000-
0002-8071-338X
Mark R. Servos −Department of Biology, University of
Waterloo, Waterloo, Ontario N2L 3G1, Canada
Gustavo Ybazeta −Health Sciences North Research Institute,
Sudbury, Ontario P3E 5J1, Canada
Marc Habash −School of Environmental Sciences, University of
Guelph, Guelph, Ontario N1G 2W1, Canada
Lawrence Goodridge −Canadian Research Institute for Food
Safety, Department of Food Science, University of Guelph,
Guelph, Ontario N1G 1Y2, Canada
Art F. Y. Poon −Department of Pathology and Laboratory
Medicine, University of Western Ontario, London, Ontario
N6A 3K7, Canada
Eric J. Arts −Department of Microbiology and Immunology,
University of Western Ontario, London, Ontario N6A 3K7,
Canada
Stephen Brown −Department of Chemistry, Queen’s
University, Kingston, Ontario K7L 3N6, Canada
Sarah Jane Payne −Department of Civil Engineering, Queen’s
University, Kingston, Ontario K7L 3N6, Canada
Andrea Kirkwood −Faculty of Science, Ontario Tech
University, Oshawa, Ontario L1G 0C5, Canada
Denina Bobbie Dawn Simmons −Faculty of Science, Ontario
Tech University, Oshawa, Ontario L1G 0C5, Canada;
orcid.org/0000-0002-9472-7192
Jean-Paul Desaulniers −Faculty of Science, Ontario Tech
University, Oshawa, Ontario L1G 0C5, Canada;
orcid.org/0000-0002-9596-4552
Banu Ormeci −Department of Civil and Environmental
Engineering, Carleton University, Ottawa, Ontario K1S 5B6,
Canada
Christopher Kyle −Department of Forensic Science, Trent
University, Peterborough, Ontario K9L 0G2, Canada
David Bulir −Department of Chemical Engineering, McMaster
University, Hamilton, Ontario L8S 4L8, Canada
Trevor Charles −Department of Biology, University of
Waterloo, Waterloo, Ontario N2L 3G1, Canada
R. Michael McKay −Great Lakes Institute for Environmental
Research, School of the Environment, University of Windsor,
Windsor, Ontario N9B 3P4, Canada; orcid.org/0000-
0003-2723-5371
K. A. Gilbride −Department of Chemistry and Biology, Toronto
Metropolitan University, Toronto, Ontario M5B 2K3, Canada
ACS ES&T Water pubs.acs.org/estwater Article
https://doi.org/10.1021/acsestwater.4c00958
ACS EST Water 2025, 5, 1605−1619
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Claire Jocelyn Oswald −Department of Geography and
Environmental Studies, Toronto Metropolitan University,
Toronto, Ontario M5B 2K3, Canada
Hui Peng −Department of Chemistry, University of Toronto,
Toronto, Ontario M5S 3ES, Canada; orcid.org/0000-
0002-2777-0588
Christopher DeGroot −Department of Mechanical and
Materials Engineering, Western University, London, Ontario
N6A 3K7, Canada
WSI Consortium
Elizabeth Renouf −Department of Civil Engineering, University
of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Complete contact information is available at:
https://pubs.acs.org/10.1021/acsestwater.4c00958
Author Contributions
N.H. led the formal analysis and manuscript writing process. All
authors contributed to the development of the RNA extraction
and RT-qPCR protocols presented in this study. All members of
the WSI Consortium contributed to the production of the
SARS-CoV-2 N1 and N2 gene and PMMoV gene quantification
in wastewater throughout the study period. P.M.D’A., E.M., and
R.D. developed the first high-sensitivity methodologies to detect
SARS-CoV-2 N1 and N2 in wastewater in Canada. P.M.D’A.,
E.M., R.D., M.S., H.D., and R.M.M. further developed the first
three methodologies for the surveillance of SARS-CoV-2, which
led to the roll-out of the Ontario WSI program. E.A.E., M.H.,
L.G., A.P., E.A., S.B., S.J.P., A.K., D.S., J.-P.D., G.Y., B.O., C.O.,
D.B., M.S., T.C., R.M.M., K.G., C.O., H.P., C.DeG., E.R., and
R.D. supervised the implementation and generation of the data
and figures in this manuscript, as well as its review and editing.
K.K.P., L.P., J.H., C.D., and E.R. contributed to the statistical
analysis, validation of methodology, and manuscript review and
editing. P.M.D’A., E.M., N.T.R., M.P.K., T.B.N., E.T., F.A.,
C.H.W., and S.W. contributed to the review and editing of this
manuscript. CRediT: Nada Hegazy formal analysis, inves-
tigation, methodology, writing - original draft; K Ken Peng
formal analysis, writing - review & editing; Patrick M. D'Aoust
data curation, writing - review & editing; Lakshmi Pisharody
writing - review & editing; Elisabeth Mercier data curation,
writing - review & editing; Nathan Thomas Ramsay writing -
review & editing; Md Pervez Kabir writing - review & editing;
Tram Bich Nguyen writing - review & editing; Emma Tomalty
writing - review & editing; Felix Addo writing - review & editing;
Chandler Hayying Wong writing - review & editing; Shen Wan
writing - review & editing; Joan Hu methodology, writing -
review & editing; Charmaine Dean methodology, writing -
review & editing; Minqing Ivy Yang data curation, writing -
review & editing; Hadi Dhiyebi data curation, writing - review &
editing; Elizabeth A. Edwards data curation, writing - review &
editing; Mark R. Servos data curation, writing - review &
editing; Gustavo Ybazeta data curation, writing - review &
editing; Marc Habash data curation, writing - review & editing;
Lawrence Goodridge data curation, writing - review & editing;
Art F. Y. Poon data curation, writing - review & editing; Eric J.
Arts data curation, writing - review & editing; Stephen Brown
data curation, writing - review & editing; Sarah Jane Payne data
curation, writing - review & editing; Andrea Kirkwood data
curation, writing - review & editing; Denina Bobbie Dawn
Simmons data curation, writing - review & editing; Jean-Paul
Desaulniers data curation, writing - review & editing; Banu
Ormeci data curation, writing - review & editing; Christopher
Kyle data curation, writing - review & editing; David Bulir data
curation, writing - review & editing; Trevor Charles data
curation, writing - review & editing; R. Michael McKay data
curation, writing - review & editing; K. A. Gilbride data
curation, writing - review & editing; Claire Jocelyn Oswald data
curation, writing - review & editing; Hui Peng data curation,
writing - review & editing; Christopher DeGroot data curation,
writing - review & editing; Elizabeth Renouf formal analysis,
methodology, validation, writing - review & editing; Robert
Delatolla funding acquisition, methodology, supervision,
validation, writing - review & editing.
Notes
The authors declare no competing financial interest.
This work was supported financially by the Province of Ontario
and the Ontario Ministry of the Environment, Conservation and
Parks (MECP) through the Ontario WSI. Additional support
was provided by the Natural Sciences and Engineering Research
Council of Canada (NSERC) Vanier Canada Graduate
Scholarship awarded to Nada Hegazy. This work was also
supported by Canada Institutes of Health Research (CIHR)
Applied Public Health Chair in Environment, Climate Change
and One Health, awarded to Robert Delatolla.
■ACKNOWLEDGMENTS
The WSI Consortium−Ontario Wastewater Surveillance
Consortium (OWSC) is a collaborative network of researchers
and academic institutions contributing to the Ontario Waste-
water Surveillance Initiative (WSI), established by the Govern-
ment of Ontario under the leadership of the Ministry of
Environment, Conservation and Parks (MECP). This initiative
was designed to advance WEM and facilitate the research,
application, and dissemination of acquired WEM data and
knowledge to inform public health decision-making. The WSI
Consortium−OWSC played a pivotal role in the data
acquisition, quality assurance, and methodological development
for all the wastewater surveillance data used in this study. This
research was made possible through the ongoing collaboration
of the OWSC member institutions across Ontario, Canada.
These institutions include the University of Ottawa, the
University of Toronto, the University of Waterloo, the
University of Guelph, the University of Western Ontario,
Queen’s University, Ontario Tech University, Health Sciences
North Research Institute, Carleton University, Trent University,
McMaster University, the University of Windsor, and Toronto
Metropolitan University. The WSI Consortium−OWSC also
acknowledges the contribution of all its members, listed below:
N.H., University of Ottawa, Ottawa, ON, Canada. P.M.D’A.,
University of Ottawa, Ottawa, ON, Canada. L.P., University of
Ottawa, Ottawa, ON, Canada. E.M., University of Ottawa,
Ottawa, ON, Canada. N.T.R., University of Ottawa, Ottawa,
ON, Canada. S.W., University of Ottawa, Ottawa, ON, Canada.
Z.Z., University of Ottawa, Ottawa, ON, Canada. E.M.R.,
University of Ottawa, Ottawa, ON, Canada. R.D., University of
Ottawa, Ottawa, ON, Canada. M.I.Y., University of Toronto,
Toronto, ON, Canada. E.E., University of Toronto, Toronto,
ON, Canada. H.P., University of Toronto, Toronto, ON,
Canada. Matthew Advani, University of Toronto, Toronto, ON,
Canada. Ronny Chan, University of Toronto, Toronto, ON,
Canada. JinJin Chen, University of Toronto, Toronto, ON,
Canada. Qinyuan (Crystal) Gong, University of Toronto,
Toronto, ON, Canada. Ismail Khan, University of Toronto,
Toronto, ON, Canada. Line Lomheim, University of Toronto,
ACS ES&T Water pubs.acs.org/estwater Article
https://doi.org/10.1021/acsestwater.4c00958
ACS EST Water 2025, 5, 1605−1619
1616
Toronto, ON, Canada. Vinthiya Paramananthasivam, University
of Toronto, Toronto, ON, Canada. Jianxian (Sunny) Sun,
University of Toronto, Toronto, ON, Canada. Endang
Susilawati, University of Toronto, Toronto, ON, Canada.
H.A.D., University of Waterloo, Waterloo, ON, Canada.
M.R.S., University of Waterloo, Waterloo, ON, Canada. T.C.,
University of Waterloo, Waterloo, ON, Canada. Simininuoluwa
Agboola, University of Waterloo, Waterloo, ON, Canada. Yash
Badlani, University of Waterloo, Waterloo, ON, Canada. Leslie
Bragg, University of Waterloo, Waterloo, ON, Canada. Patrick
Breadner, University of Waterloo, Waterloo, ON, Canada.
Hoang Dang, University of Waterloo, Waterloo, ON, Canada.
Rachel Dawe, University of Waterloo, Waterloo, ON, Canada.
Isaac Ellmen, University of Waterloo, Waterloo, ON, Canada.
J.A.F., University of Waterloo, Waterloo, ON, Canada. Meghan
Fuzzen, University of Waterloo, Waterloo, ON, Canada. Alice
Gere, University of Waterloo, Waterloo, ON, Canada. Blake
Haskell, University of Waterloo, Waterloo, ON, Canada. Samina
Hayat, University of Waterloo, Waterloo, ON, Canada. Hannifer
Ho, University of Waterloo, Waterloo, ON, Canada. Yemurayi
Hungwe, University of Waterloo, Waterloo, ON, Canada.
Heather Ikert, University of Waterloo, Waterloo, ON, Canada.
Jennifer Knapp, University of Waterloo, Waterloo, ON, Canada.
Su-Hyun Kwon, University of Waterloo, Waterloo, ON, Canada.
Ria Menon, University of Waterloo, Waterloo, ON, Canada.
Zach Miller, University of Waterloo, Waterloo, ON, Canada.
Shiv Naik, University of Waterloo, Waterloo, ON, Canada.
Delaney Nash, University of Waterloo, Waterloo, ON, Canada.
Anthony Ng, University of Waterloo, Waterloo, ON, Canada.
Alyssa Overton, University of Waterloo, Waterloo, ON, Canada.
Carly Sing-Judge, University of Waterloo, Waterloo, ON,
Canada. Nivetha Srikanthan, University of Waterloo, Waterloo,
ON, Canada. K.W., University of Waterloo, Waterloo, ON,
Canada. Eli Zeeb, University of Waterloo, Waterloo, ON,
Canada. G.Y., Health Sciences North Research Institute,
Sudbury, ON, Canada. Dania Andino, Health Sciences North
Research Institute, Sudbury, ON, Canada. James Knockleby,
Health Sciences North Research Institute, Sudbury, ON,
Canada. Aleksandra M. Mloszewska, Health Sciences North
Research Institute, Sudbury, ON, Canada. Ataollah Mohamma-
diankia, Health Sciences North Research Institute, Sudbury,
ON, Canada. M.H., University of Guelph, Guelph, ON, Canada.
L.G., University of Guelph, Guelph, ON, Canada. Opeyemi U.
Lawal, University of Guelph, Guelph, ON, Canada. Valeria R.
Parreira, University of Guelph, Guelph, ON, Canada. Samran
Prasla, University of Guelph, Guelph, ON, Canada. Melinda
Precious, University of Guelph, Guelph, ON, Canada. Fozia
Rizvi, University of Guelph, Guelph, ON, Canada. A.P., Western
University, London, ON, Canada. E.A., Western University,
London, ON, Canada. Adebowale Adebiyi, Western University,
London, ON, Canada. C.DeG., Western University, London,
ON, Canada. Justin Donovan, Western University, London,
ON, Canada. Richard Gibson, Western University, London,
ON, Canada. Amanda Hamilton, Western University, London,
ON, Canada. Dilan Joseph, Western University, London, ON,
Canada. Abayomi Olabode, Western University, London, ON,
Canada. Gopi Gugan, Western University, London, ON,
Canada. Jessica Pardy, Western University, London, ON,
Canada. Dan Siemon, Western University, London, ON,
Canada. R.S.B., Queen’s University, Kingston, ON, Canada.
S.J.P., Queen’s University, Kingston, ON, Canada. A.K., Ontario
Tech University, Oshawa, ON, Canada. D.S., Ontario Tech
University, Oshawa, ON, Canada. J.-P.D., Ontario Tech
University, Oshawa, ON, Canada. Linda Lara-Jacobo, Ontario
Tech University, Oshawa, ON, Canada. Tyler Dow, Ontario
Tech University, Oshawa, ON, Canada. Matthew Cranney,
Ontario Tech University, Oshawa, ON, Canada. Tomas de
Melo, Ontario Tech University, Oshawa, ON, Canada. Nancy
Tannouri, Ontario Tech University, Oshawa, ON, Canada.
Ashley Gedge, Ontario Tech University, Oshawa, ON, Canada.
Golam Islam, Ontario Tech University, Oshawa, ON, Canada.
B.O., Carleton University, Ottawa, ON, Canada. Lena Carolin
Bitter, Carleton University, Ottawa, ON, Canada. Tim Garant,
Carleton University, Ottawa, ON, Canada. Richard Kibbee,
Carleton University, Ottawa, ON, Canada. Gabriela Jimenez
Pabon, Carleton University, Ottawa, ON, Canada. C.K., Trent
University, Peterborough, ON, Canada. D.B., McMaster
University, Hamilton, ON, Canada. Jodi Gilchrist, McMaster
University, Hamilton, ON, Canada. Sarah Marttala, McMaster
University, Hamilton, ON, Canada. Ian Restall, McMaster
University, Hamilton, ON, Canada. Doris Williams, McMaster
University, Hamilton, ON, Canada. Danielle Sobers, McMaster
University, Hamilton, ON, Canada. Ryland Corchis-Scott,
University of Windsor, Windsor, ON, Canada. Quidi Geng,
University of Windsor, Windsor, ON, Canada. R.M.M.,
University of Windsor, Windsor, ON, Canada. K.G., Toronto
Metropolitan University, Toronto, ON, Canada. C.O., Toronto
Metropolitan University, Toronto, ON, Canada. Menglu L.
Wang, Toronto Metropolitan University, Toronto, ON,
Canada. Arthur Tong, Toronto Metropolitan University,
Toronto, ON, Canada. Diego Orellano, Toronto Metropolitan
University, Toronto, ON, Canada. Hussain Aqeel, Toronto
Metropolitan University, Toronto, ON, Canada. Babneet
Channa, Toronto Metropolitan University, Toronto, ON,
Canada. Nora Danna, Toronto Metropolitan University,
Toronto, ON, Canada. Farnaz Farahbakhsh, Toronto Metro-
politan University, Toronto, ON, Canada. Eyerusalem Goitom,
Toronto Metropolitan University, Toronto, ON, Canada.
Matthew Santilli, Toronto Metropolitan University, Toronto,
ON, Canada. Hooman Sarvi, Toronto Metropolitan University,
Toronto, ON, Canada. Amir Tehrani, Toronto Metropolitan
University, Toronto, ON, Canada. Vince Pileggi, Ministry of the
Environment, Conservation and Parks (MECP), Toronto, ON,
Canada. The WSI Consortium−OWSC gratefully acknowledges
the vital contributions of all employees from the Province of
Ontario, the MECP, and the Ontario Ministry of Health. A
special acknowledgement also goes out to the partner municipal
governments and the employees of all municipalities and public
health units involved in the surveillance program, whose eorts
were essential to its success.
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ACS ES&T Water pubs.acs.org/estwater Article
https://doi.org/10.1021/acsestwater.4c00958
ACS EST Water 2025, 5, 1605−1619
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