Huo Dazhu, Zhang Ting, Han Xuan, Yang Liuyang, Wang Lei, Fan Ziliang, Wang Xiaoli, Yang Jiao, Huang Qiangru, Zhang Ge, Wang Ye, Qian Jie, Sun Yanxia, Qu Yimin, Li Yugang, Ye Chuchu, Feng Luzhao, Li Zhongjie, Yang Weizhong, Wang Chen
School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
China CDC Wkly. 2024 Sep 13;6(37):939-945. doi: 10.46234/ccdcw2024.195.
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.
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.
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.
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.
传染病给全球健康和经济带来了巨大负担,凸显了对精确预测模型的迫切需求。百度指数提供了增强的实时监测能力,可对传统系统进行补充。
从2022年11月至2023年1月,提取了中国255个城市中百度搜索引擎关于关键词“发热”的数据。通过测试结合阈值和连续天数的各种标准,确定流感疫情的发病和高峰日期。
指示疫情开始的最有效方案是连续七天超过第90百分位数阈值,从而将误报降至最低。使用7天移动平均线对峰值检测进行了优化,平衡了稳定性和精度。
使用诸如百度指数之类的互联网搜索数据,可显著提高疾病监测模型的及时性和准确性。这种创新方法有助于更快地开展公共卫生干预措施,并展示了其在加强疫情监测和应对工作方面的潜力。