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 affect 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.