Currently, data on foodborne outbreaks in Canada is quite limited and this model will help identify areas of concern that would not normally be considered a source of foodborne pathogens or causing outbreaks.
What challenge does “A Modeling Framework to Predict Foodborne Outbreaks” address
The project’s purpose is to create a database of key words and descriptors from Canadian foodborne outbreaks (from all provinces and territories) along with other data sources (such as weather, pharmacy sales, travel data, data regarding clinical outbreaks in animals that can cause outbreaks/illnesses in humans, food import data) to build a model that will be used to predict potential foodborne outbreaks and emergence of foodborne pathogens in Canada. Moreover, the research team is also building a taxonomy of foodborne outbreaks to help understand how they occur. Currently, no such model or taxonomy exists and given the rise in foodborne outbreaks, using this model and taxonomy to see trends and patterns in historical data could be a useful tool for prediction of foodborne outbreaks/pathogens or at least areas where attention should be focused.
How will this research address the challenge?
Currently, the research team is gathering data of previous outbreaks in Canada to create a database that will be used to create this prediction model with the help of computer and artificial intelligence scientists.
What impact will the project have on agriculture?
This project will impact the food safety landscape by potentially identifying areas/sources of foodborne pathogens, which can be monitored to prevent an outbreak. Additionally, this model can find trends/patterns in historical data that are not easily seen by conventional reviews. Developing a taxonomy of foodborne outbreaks also helps in investigations and asking the right questions.
This project will help with surveillance and future research of foodborne outbreaks and/or foodborne pathogens. Currently, data on foodborne outbreaks in Canada is quite limited and this model will help identify areas of concern that would not normally be considered a source of foodborne pathogens or causing outbreaks. Moreover, being able to predict foodborne outbreaks before they occur, and as such addressing gaps in food safety could limit the potential for infection and subsequent illness. This project also acts as a good surveillance tool for foodborne outbreaks and foodborne pathogens.
Partners: Unviersity of Guelph (Food Science Department, Computer Science Department), IBM, Public Health Agency of Canda, Public Health Ontario, British Columbia Centre for Disease Control.
Collaborators and students: Dr. Kavita Walia, Jingjing Wang, Dr. Rozita Dara, Dr. Fei Song, Dr. Jan Sargeant, Dr. Kate Thomas.