Abstracts are provided in the language submitted.

A major impediment to detection of infectious disease outbreaks is the Surveillance Continuum which relies on sick people to seek medical help. Within the context of the current COVID-19 Omicron wave, many sick individuals do not seek medical help meaning that accurate case numbers are unknown in Ontario and across Canada. In addition, the lack of rapid antigen and PCR tests have led to underreporting of cases. Active surveillance approaches are needed to better estimate the true number of COVID-19 infections. We have developed a novel, integrated approach for population level COVID-19 case detection, by combining real-time polymerase chain reaction (RT-PCR) based detection of SARS-CoV-2 in raw (influent) wastewater, with monitoring of social media for keywords associated with COVID-19 (social media syndromic analysis). An increase in signals observed in the wastewater and/or on social media allows for identification of communities where COVID-19 cases are rising. Since September of 2020, we have monitoring the presence of COVID-19 on the University of Guelph Campus using wastewater based epidemiology (WBE). In this talk, we highlight how the combination of WBE and social media syndromic analysis can enhance rapid identification of COVID-19. Additionally, the use of metagenomic whole genome sequencing approaches to characterize the presence of SARS-CoV-provides virus sequences from wastewater that can be used to track the emergence of new SARS-CoV-2 variants of concern such as Omicron, potentially before clinical testing reveals their presence in the community.

Dr. Lawrence Goodridge