Impact of coagulation on SARS-CoV-2 and PMMoV viral signal in wastewater solids (preprint)

Wastewater surveillance (WWS) has received interest from researchers, scientists, and public health units for its application in monitoring active COVID-19 cases and detecting outbreaks. While WWS of SARS-CoV-2 has been widely applied worldwide, a knowledge gap exists concerning the effects of enhanced primary clarification, the application of coagulant to primary clarifiers, on SARS-CoV-2 and PMMoV quantification for reliable wastewater-based epidemiology. Ferric-based chemical coagulants are extensively used in enhanced clarification, particularly for phosphorus removal, in North America, and Europe. This study examines the effects of coagulation with ferric sulfate on the measurement of SARS-CoV-2 and PMMoV viral measurements in wastewater primary sludge and hence also settled solids. The addition of Fe3+ to wastewater solids ranging from 0 to 60 mg/L caused no change in N1 and N2 gene region measurements in wastewater solids, where Fe3+ concentrations in primary clarified sludge represent the conventional minimum and maximum concentrations of applied ferric-based coagulant. However, elevated Fe3+ concentrations were shown to be associated with a statistically significant increase in PMMoV viral measurements in wastewater solids, which consequently resulted in the underestimation of PMMoV normalized SARS-CoV-2 viral signal measurements (N1 and N2 copies/copies of PMMoV). pH reduction from coagulant addition did not contribute to the increase in PMMoV measurements. Thus, this phenomenon is likely attributed to the partitioning of PMMoV particles to the solids of wastewater from the bulk liquid phase of wastewater.

Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic (preprint)

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns.

Wastewater surveillance of influenza activity: Early detection, surveillance, and subtyping in city and neighbourhood communities (preprint)

Recurrent epidemics of influenza infection and its pandemic potential present a significant risk to global population health. To mitigate hospitalizations and death, local public health relies on clinical surveillance to locate and monitor influenza-like illnesses and/or influenza cases and outbreaks. At an international level, the global integration of clinical surveillance networks is the only reliable method to report influenza types and subtypes and warn of an emergent pandemic strain. During the COVID-19 pandemic, the demonstrated utility of wastewater surveillance (WWS) in complementing or even replacing clinical surveillance, the latter a resource-intensive enterprise, was predicated on the presence of stable viral fragments in wastewater. We show that influenza virus targets are stable in wastewaters and partitions to the solids fraction. We subsequently quantify, type, and subtype influenza virus in municipal wastewater and primary sludge throughout the course of a community outbreak. This research demonstrates the feasibility of applying influenza virus WWS to city and neighbourhood levels; showing a 17-day lead time in forecasting a citywide flu outbreak and providing population-level viral subtyping in near real-time using minimal resources and infrastructure.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities (preprint)

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the extant and anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) is likely to have greater value as an important diagnostic tool to inform public health. As the widespread adoption of WWS is relatively new at the scale employed for COVID-19, interpretation of data, including the relationship to clinical cases, has yet to be standardized. An in-depth analysis of the metrics derived from WWS is required for public health units/agencies to interpret and utilize WWS-acquired data effectively and efficiently. In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven different cities in Canada over periods ranging from 8 to 21 months. Significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing in these communities. The WC ratio decreased significantly during the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized communitys wastewater (40-60% allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized communitys wastewater (40-60% allelic proportion). Finally, a rapid and significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variants greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when vaccine-induced community immunity was high. The WC ratio, used as an additional monitoring metric, complements clinical case counts and wastewater signals as individual metrics in its ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age. (preprint)

Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.

A Wastewater-Based Epidemic Model for SARS-CoV-2 with Application to Three Canadian Cities (preprint)

1The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.

COVID-19 monitoring in rural communities: First comparison of lagoon and pumping station samples for wastewater-based epidemiology (preprint)

Wastewater-based epidemiology is a topic of significant interest over the last year due to the application of SARS-CoV-2 surveillance to track incidence rates of COVID-19 in communities. Although SARS-CoV-2 surveillance has been applied in more than 50 countries to date, the application of this surveillance has been largely focused on relatively affluent urban and peri-urban communities. As such, there is a lack of knowledge regarding the implementation of reliable wastewater surveillance in small and rural communities for the purpose of tracking rates of incidence of COVID-19 and other pathogens or biomarkers. This study examines the correlations between SARS-CoV-2 RNA signal from wastewater samples harvested from the access port at a lagoon (waste-stabilization pond), a wastewater pumping station and the regional COVID-19 rate of incidence (measured as percent test positivity) in a small, rural community in Ontario, Canada. Real-time quantitative polymerase chain reaction (RT-qPCR) targeting the N1 and N2 genes of SARS-CoV-2 of lagoon samples demonstrate that 80% of 24-hr composite samples collected across a period of 5.5 weeks were below the limit of quantification (5 gene copies/microliter). However, 100% of the 24-hr composite samples collected on the same days from the upstream pumping station were capable of not only yielding strong viral signal but once normalized for PMMoV, also predicted the increase in viral signal approximately 10-14 days prior to an increase in community's COVID-19 reported test percent positivity. RNA concentration and integrity of samples harvested from the lagoon was both lower and more variable than from RNA harvested from the upstream pumping station that were collected on the same date, indicating a higher overall stability of SARS-CoV-2 RNA and hence a stronger viral signal that correlates to community incidence of COVID-19. In sewered small and rural communities operating wastewater lagoons, WBE samples should therefore be harvested from pumping stations or the sewershed as opposed to lagoons.

An allele-specific primer extension assay to quantify the proportion of B.1.1.7-specific SARS-CoV-2 RNA in wastewater (preprint)

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed millions of lives globally to date. Rapid accumulation of co-occurring mutations has led to the emergence of viral variants which appear to be more transmissible, virulent, or both. Variants of concern (VOC) now include those belonging to the B.1.1.7, B.1.351, and P.1 lineages. Early detection of VOC and the ability to retrospectively follow their respective, longitudinal prevalence in communities is wanting. Wastewater-based epidemiology (WBE) allows tracking of disease prevalence in the general population using RT-qPCR to detect viral fragments, but ongoing longitudinal studies have yet to differentiate between these variants. Here, we describe and validate a primer extension strategy to amplify and distinguish B.1.1.7-specific, from non-B.1.1.7 alleles by combining new forward, and existing CDC 2019-nCoV_N1 qPCR probes and reverse primers. This assay can be quickly implemented within a current SARS- CoV-2 WBE framework with minimal cost with the goal of providing early detection of increasing B.1.1.7 transmission in a community prior to identification through clinical testing and confirmation via secondary screening strategies. As such, this assay can provide public health units with an additional and much needed metric to be able to rapidly triangulate B.1.1.7 prevalence.

A repurposed, blood gene signature is associated with poor outcomes in SARS-CoV-2 (preprint)

Poor outcomes after SARS-CoV-2 infection are difficult to predict. Survivors may develop pulmonary fibrosis. We previously identified a 52-gene signature in peripheral blood, predictive of mortality in Idiopathic Pulmonary Fibrosis. In this study, we analyzed this signature in SARS-CoV-2 infected individuals and identified genomic risk profiles with significant differences in outcomes. Analysis of single cell expression data shows that monocytes, red blood cells, neutrophils and dendritic cells are the cellular source of the high risk gene signature.

The multidisciplinary nature of COVID-19 research (preprint)

Objective: We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue. Design/Methodology/Approach: We examined bibliometric measures for COVID-19 and the rest of studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages. Results/Discussion: The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation. Conclusions: COVID-19 literature showed the broadest subject coverage, which is clearly a consecuence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics. Originality/Value: The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective.

Catching a resurgence: Increase in SARS-CoV-2 viral RNA identified in wastewater 48 hours before COVID-19 clinical tests and 96 hours before hospitalizations (preprint)

Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world. Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and facile surveillance tool. However, their efficacy in prospectively capturing resurgence following a period of low prevalence is unclear. In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawas water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%. In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections. During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours. This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.

N439K variant in spike protein may alter the infection efficiency and antigenicity of SARS-CoV-2 based on molecular dynamics simulation (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), has been undergoing various mutations. The analysis of the structural and energetic effects of mutations on protein-protein interactions between the receptor binding domain (RBD) of SARS-CoV-2 and angiotensin converting enzyme 2 (ACE2) or neutralizing monoclonal antibodies will be beneficial for epidemic surveillance, diagnosis, and optimization of neutralizing agents. According to the molecular dynamics simulation, a key mutation N439K in the SARS-CoV-2 RBD region created a new salt bridge which resulted in greater electrostatic complementarity. Furthermore, the N439K-mutated RBD bound hACE2 with a higher affinity than wild-type, which may lead to more infectious. In addition, the N439K-mutated RBD was markedly resistant to the SARS-CoV-2 neutralizing antibody REGN10987, which may lead to the failure of neutralization. These findings would offer guidance on the development of neutralizing antibodies and the prevention of COVID-19.

SARS-CoV-2 Protein in Wastewater Mirrors COVID-19 Prevalence. (preprint)

The COVID-19 pandemic has given rise to diverse approaches to track infections. The causative agent, SARS-CoV-2 is a fecally-shed RNA virus, and many groups have assayed wastewater for viral RNA fragments by quantitative reverse transcription polymerase chain reaction (qRT-PCR) as a proxy of COVID-19 prevalence in the community. Most groups report low levels of viral RNA that often skirt the methods theoretical limits of detection and quantitation. Here, we demonstrate the presence of SARS-CoV-2 structural proteins in wastewater using traditional immunoblotting and quantitate them from wastewater solids using an immuno-linked PCR method called Multiplex Paired-antibody Amplified Detection (MPAD). In this longitudinal study, we corrected for stochastic variability inherent to wastewater-based epidemiology using multiple fecal content protein biomarkers. These normalized SARS-CoV-2 protein data correlated well with public health metrics. Our method of assaying SARS-CoV-2 protein from wastewater represents a promising and sensitive epidemiological tool to assess prevalence of fecally-shed pathogens in the community.

Quantitative analysis of SARS-CoV-2 RNA from wastewater solids in communities with low COVID-19 incidence and prevalence (preprint)

In the absence of an effective vaccine to prevent COVID-19 it is important to be able to track community infections to inform public health interventions aimed at reducing the spread and therefore reduce pressures on health-care units, improve health outcomes and reduce economic uncertainty. Wastewater surveillance has rapidly emerged as a potential tool to effectively monitor community infections for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), through measuring trends of viral RNA signal in wastewater systems. In this study SARS-CoV-2 viral RNA N1 and N2 genes are quantified in solids collected from influent post grit solids (PGS) and primary clarified sludge (PCS) in two water resource recovery facilities (WRRF) serving Canadas national capital region, i.e., the City of Ottawa, ON (pop. {approx} 1.1M) and the City of Gatineau, QC (pop. {approx} 280K). PCS samples show signal inhibition using RT-ddPCR compared to RT-qPCR, with PGS samples showing similar quantifiable concentrations of RNA using both assays. RT-qPCR shows higher frequency of detection of N1 and N2 genes in PCS (92.7, 90.6%) as compared to PGS samples (79.2, 82.3%). Sampling of PCS may therefore be an effective approach for SARS-CoV-2 viral quantification, especially during periods of declining and low COVID-19 incidence in the community. The pepper mild mottle virus (PMMV) is determined to have a less variable RNA signal in PCS over a three month period for two WRRFs, regardless of environmental conditions, compared to Bacteroides 16S rRNA or human eukaryotic 18S rRNA, making PMMV a potentially useful biomarker for normalization of SARS-CoV-2 signal. PMMV-normalized PCS RNA signal from WRRFs of two cities correlated with the regional public health epidemiological metrics, identifying PCS normalized to a fecal indicator (PMMV) as a potentially effective tool for monitoring trends during decreasing and low-incidence of infection of SARS-Cov-2 in communities.