摘要
食源性疾病由于其症状轻重不一常被低估,但近年来,食源性疾病的爆发在全国范围内呈上升趋势,准确探测食源性疾病事件并对其进行风险评估有重要意义.本文分别对哨点医院监测数据、食品检测数据和来自互联网的数据建立事件探测模型,实现风险评估,并分析比较模型优劣,最后建立统一的时空框架,引入人口、交通、食品生产等大数据对风险预测结果进行综合集成.通过对某大城市2014年食源性疾病事件的探测结果对比,实证结果表明,综合模型预测的时空精度更高,对防控更具操作性.
Foodborne diseases are usually underreported due to its various symptoms. It has emerged to be a critical burden of public health in China. Auto detection of foodborne disease event and risk assessment based on it are helpful to prevent and control its outbreak. We design three different event detection models according to three different kinds of data from disease surveillance system, food detection system and social media. By the integrated spatio-temporal data framework and the imported big-data of population, traffic, and food production and sales, the final model performs better than the isolated ones in spatial and temporal dimensions. This is testified by the results on the data of one city of China in 2014.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2015年第10期2523-2530,共8页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(41371386
91224006)
卫生部行业专项(201302005)
关键词
食源性疾病
大数据
事件探测
风险评估
集成时空框架
foodborne disease
big data
event detection
risk assessment
integrated spatio-temporal framework
