Wastewater surveillance (WWS), included in the field of study of wastewater-based epidemiology (WBE), is the analysis of wastewaters to quantify community disease or use of chemicals by the community, such as pharmaceuticals and illicit drugs. WWS has historically been applied within the context of community public health through monitoring of pathogenic viral outbreaks such as polio and hepatitis A, as well as monitoring of illicit drug consumption. While WWS has been used for several decades, many of its contributions were largely unpopular within the public mainstream prior to the coronavirus disease in 2019 (COVID-19) pandemic. Since the onset of the pandemic, public health resources around the world were significantly afflicted by COVID-19. This elicited a prompt response by researchers to rapidly develop WWS for the application of severe acute respiratory syndrome-2 virus (SARS-CoV-2) WWS as a complementary epidemiological tool for population-wide monitoring of COVID-19 outbreaks. With the novelty of this technology, there are several challenges and gaps of knowledge that remain to be addressed in order to improve the reliability of WWS for SARS-CoV-2. Particularly, the effects of various constituents, endogenous and added, that commonly occur and are applied to wastewaters may result in the significant variability observed in WWS data sets, which in turn results in the uncertainty of the interpretation of WWS data sets of SARS-CoV-2 by various public health agencies throughout the pandemic. This study is aimed to address the critical issue of data variability by investigating the effect of enhanced primary clarification with ferric-based chemical coagulants on the measurements of SARS-CoV-2 and the pepper mild mottle virus (PMMoV) WWS normalizing biomarker. It is believed that the addition of ferric ions via common coagulation treatment of primary sludge would interfere with the quantitative polymerase chain reaction (qPCR) amplification of viral RNA and could cause false-negative results. With 18.1% of the total population in Canada receiving wastewater that undergoes primary treatment including chemical precipitation/flocculation, and with proof of enrichment of SARS-CoV-2 and PMMoV RNA in untreated wastewater and settled primary sludge, it is important to elucidate whether ferric sulfate chemical coagulant is a potential source of data variability for population-wide WWS. With ferric sulfate concentrations ranging from 0 - 60 mg/L as Fe³⁺, the PMMoV-normalized SARS-CoV-2 viral signal measurements were significantly reduced as a result of a significant elevation in the PMMoV viral signal measurements. This is possibly due to the partitioning of PMMoV viral particles from the liquid phase to the solids phase of wastewater samples influenced by ferric sulfate at 60 mg/L as Fe³⁺ compared to the samples that were not treated with ferric sulfate. This thesis also examined the evolving relation of WWS measurements to measurements of public health metrics to improve our current interpretation of SARS-CoV-2 WWS. The statistical correlations between wastewater PMMoV-normalized SARS-CoV-2 viral signal and clinical metrics indicative of disease incidence (laboratory-confirmed COVID-19 positive cases), and metrics indicative of disease burden (hospitalization, intensive care unit (ICU) admissions, and deaths) are investigated from the onset of the wildtype and the Alpha variant of concern (VOC) during limited vaccination immunization, through the onset of the Omicron BA.2 VOC in two strongly characterized sewersheds (Ottawa and Hamilton). WWS demonstrates to be a strong indicator of both disease incidence and disease burden during the period of limited vaccination immunization, and a moderate indicator of disease incidence, while remains a strong indicator of disease burden during the period of peak vaccination immunization (2-4 weeks after reception of 2 doses of the COVID-19 vaccine). Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44415 |
| Date | 20 December 2022 |
| Creators | Hegazy, Nada |
| Contributors | Delatolla, Robert, Guilherme, Stephanie |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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