Social media analytics for early detection of foodborne disease

食源性疾病已经成为一种严重的、未得到充分报道的公共卫生问题，具有高昂的健康和经济成本。世界卫生组织(世卫组织)将食源性疾病暴发确定为21世纪的一个主要全球公共卫生威胁。传统的监测系统，如加拿大可通报疾病监测系统，只捕获了加拿大每年约400万食源性疾病病例中的一小部分。它们依赖于大量指标的收集，包括临床症状、病毒学流产结果、住院人数和死亡统计数据，导致从临床医生向卫生部门提交报告的中位数延迟6.5天。公共卫生决策者认为延迟通报是调查食源性疾病的障碍，因为它可能在地理上分布在很远的地方。及早发现食源性疾病可以通过从零售和餐饮服务场所移除受污染的产品，提高公众意识，并为接触者提供更及时的预防和治疗措施，从而减少接触者的数量。我们建议，应利用社交媒体数据作为传统监测系统的补充部分。在大型用户群体中传播的信息的巨大和高度变化性为挖掘食源性疾病活动信号的数据、定性和定量分析疾病飞溅以及预测未来暴发提供了机会。我们提出了一系列基于社交媒体的预测模型，通过对用户在社交媒体上的对话进行跟踪和监控，来表征和检测即将到来的食源性疾病爆发。其目的是促进非传统来源的食源性疾病检测的研究，为卫生决策者提供情景意识。

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.