Notifiable infectious diseases are a major public health concern in China,causing about five million illnesses and twelve thousand deaths every year.Early detection of disease activity,when followed by a rapid respons...Notifiable infectious diseases are a major public health concern in China,causing about five million illnesses and twelve thousand deaths every year.Early detection of disease activity,when followed by a rapid response,can reduce both social and medical impact of the disease.We aim to improve early detection by monitoring health-seeking behavior and disease-related news over the Internet.Specifically,we counted unique search queries submitted to the Baidu search engine in 2008 that contained disease-related search terms.Meanwhile we counted the news articles aggregated by Baidu's robot programs that contained disease-related keywords.We found that the search frequency data and the news count data both have distinct temporal association with disease activity.We adopted a linear model and used searches and news with 1–200-day lead time as explanatory variables to predict the number of infections and deaths attributable to four notifiable infectious diseases,i.e.,scarlet fever,dysentery,AIDS,and tuberculosis.With the search frequency data and news count data,our approach can quantitatively estimate up-to-date epidemic trends 10–40 days ahead of the release of Chinese Centers for Disease Control and Prevention(Chinese CDC)reports.This approach may provide an additional tool for notifiable infectious disease surveillance.展开更多
目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发...目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。展开更多
文摘Notifiable infectious diseases are a major public health concern in China,causing about five million illnesses and twelve thousand deaths every year.Early detection of disease activity,when followed by a rapid response,can reduce both social and medical impact of the disease.We aim to improve early detection by monitoring health-seeking behavior and disease-related news over the Internet.Specifically,we counted unique search queries submitted to the Baidu search engine in 2008 that contained disease-related search terms.Meanwhile we counted the news articles aggregated by Baidu's robot programs that contained disease-related keywords.We found that the search frequency data and the news count data both have distinct temporal association with disease activity.We adopted a linear model and used searches and news with 1–200-day lead time as explanatory variables to predict the number of infections and deaths attributable to four notifiable infectious diseases,i.e.,scarlet fever,dysentery,AIDS,and tuberculosis.With the search frequency data and news count data,our approach can quantitatively estimate up-to-date epidemic trends 10–40 days ahead of the release of Chinese Centers for Disease Control and Prevention(Chinese CDC)reports.This approach may provide an additional tool for notifiable infectious disease surveillance.
文摘目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。
