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作 者:Dazhu Huo Ting Zhang Xuan Han Liuyang Yang Lei Wang Ziliang Fan Xiaoli Wang Jiao Yang Qiangru Huang Ge Zhang Ye Wang Jie Qian Yanxia Sun Yimin Qu Yugang Li Chuchu Ye Luzhao Feng Zhongjie Li Weizhong Yang Chen Wang
机构地区:[1]School of Health Policy and Management,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing,China [2]School of Population Medicine and Public Health,Chinese Academy of Medical Sciences(CAMS)&Peking Union Medical College,Beijing,China [3]Yichang Center for Disease Prevention and Control,Yichang City,Hubei Province,China [4]Weifang Center for Disease Prevention and Control,Weifang City,Shandong Province,China [5]Beijing Center for Disease Prevention and Control,Beijing,China [6]School of Public Health,Dali University,Dali City,Yunnan Province,China [7]Shanghai Pudong New Area Center for Disease Control and Prevention,Shanghai,China
出 处:《China CDC weekly》2024年第37期939-945,I0001-I0007,共14页中国疾病预防控制中心周报(英文)
基 金:Supported by grants from the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-044,2023-I2M-3-011);National Key Research and Development Program of China(2023YFC2308701).
摘 要:Introduction:Infectious diseases pose a significant global health and economic burden,underscoring the critical need for precise predictive models.The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems.Methods:Baidu search engine data on the keyword“fever”were extracted from 255 cities in China from November 2022 to January 2023.Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days.Results:The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days,minimizing false starts.Peak detection was optimized using a 7-day moving average,balancing stability and precision.Discussion:The use of internet search data,such as the Baidu index,significantly improves the timeliness and accuracy of disease surveillance models.This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.
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