In Canada, consumption of contaminated food causes 4 million illnesses, 14,150 hospitalizations and 323 deaths each year, with an estimated annual economic burden of approximately $4 billion, and a major impediment to the identification of contaminated food is that current surveillance methods rely on sick people to seek medical help. The Public Health Agency of Canada (PHAC), in partnership with the University of Guelph and Université Laval, aims to develop a novel, integrated approach to improved foodborne outbreak detection, beginning with metagenomic detection of foodborne pathogens in raw sewage within geographically localized monitoring sites (Quebec City, Guelph, Winnipeg), and monitoring of social media for keywords associated with enteric illness. The tools, methods and datasets generated through this project will be translated for downstream operational use into the network of Canadian foodborne surveillance programs through collaborations between PHAC and its federal/provincial/territorial partners. Implementation is expected to result in a reduction in the amount of illnesses and hospitalizations and economic savings due to a reduction in food recalls through faster detection of outbreaks. A key advantage of this flexible ‘omics and social media surveillance approach is that it can be scaled for rapid detection of other pathogens, and will be immediately utilized to monitor levels of SARS-CoV-2 (the COVID-19 virus) in wastewater, as an early indicator of changing case numbers prior to clinical presentation.