Reference number : GC-2017-Q1-00394, amendment number : , agreement type : G, recipient legal name : Bagheri, Ebrahim (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Foodborne Disease has emerged as a serious and underreported public health problem with high health and_x000D_
financial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major global_x000D_
public health threat in the twenty-first century. Traditional surveillance systems such as Canadian Notifiable_x000D_
Disease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborne_x000D_
illness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virology_x000D_
laboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days between_x000D_
case report from clinicians to the health departments. Public health decision-makers consider the delayed_x000D_
notification as a barrier to investigating foodborne disease, as it can potentially distribute geographically across_x000D_
great distances. Early detection of foodborne disease can reduce the number of exposed individuals by_x000D_
removing contaminated product from retail and foodservice outlets, increasing public awareness, and offering a_x000D_
more timely preventative and therapeutic measures to exposed individuals._x000D_
We propose that social media data should be exploited as a complementary component of the traditional_x000D_
surveillance systems. The enormity and high variance of the information that propagates through large user_x000D_
communities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illness_x000D_
patterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of social_x000D_
media-based predictive models to characterize and detect upcoming foodborne illness outbreaks through_x000D_
ambient tracking and monitoring over users' conversations in social media. The objective is to advance research_x000D_
on foodborne disease detection from non-traditional sources to supply health decision makers with situational_x000D_
awareness., program purpose french : Foodborne Disease has emerged as a serious and underreported public health problem with high health and_x000D_
financial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major global_x000D_
public health threat in the twenty-first century. Traditional surveillance systems such as Canadian Notifiable_x000D_
Disease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborne_x000D_
illness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virology_x000D_
laboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days between_x000D_
case report from clinicians to the health departments. Public health decision-makers consider the delayed_x000D_
notification as a barrier to investigating foodborne disease, as it can potentially distribute geographically across_x000D_
great distances. Early detection of foodborne disease can reduce the number of exposed individuals by_x000D_
removing contaminated product from retail and foodservice outlets, increasing public awareness, and offering a_x000D_
more timely preventative and therapeutic measures to exposed individuals._x000D_
We propose that social media data should be exploited as a complementary component of the traditional_x000D_
surveillance systems. The enormity and high variance of the information that propagates through large user_x000D_
communities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illness_x000D_
patterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of social_x000D_
media-based predictive models to characterize and detect upcoming foodborne illness outbreaks through_x000D_
ambient tracking and monitoring over users' conversations in social media. The objective is to advance research_x000D_
on foodborne disease detection from non-traditional sources to supply health decision makers with situational_x000D_
awareness., agreement title english : Social media analytics for early detection of foodborne disease, agreement title french : Social media analytics for early detection of foodborne disease, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada