Social media analytics for early detection of foodborne disease

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Foodborne Disease has emerged as a serious and underreported public health problem with high health andfinancial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major globalpublic health threat in the twenty-first century. Traditional surveillance systems such as Canadian NotifiableDisease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborneillness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virologylaboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days betweencase report from clinicians to the health departments. Public health decision-makers consider the delayednotification as a barrier to investigating foodborne disease, as it can potentially distribute geographically acrossgreat distances. Early detection of foodborne disease can reduce the number of exposed individuals byremoving contaminated product from retail and foodservice outlets, increasing public awareness, and offering amore timely preventative and therapeutic measures to exposed individuals.We propose that social media data should be exploited as a complementary component of the traditionalsurveillance systems. The enormity and high variance of the information that propagates through large usercommunities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illnesspatterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of socialmedia-based predictive models to characterize and detect upcoming foodborne illness outbreaks throughambient tracking and monitoring over users' conversations in social media. The objective is to advance researchon foodborne disease detection from non-traditional sources to supply health decision makers with situationalawareness.