Yang Kai, Fan Shuangfeng, Deng Jiali, Xia Jinjie, Hu Xiaoyuan, Yu Linlin, Wang Bin, Yu Wei
Chengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu Center for Disease Control and Prevention, Chengdu, China.
Department of Orthopaedics, The First Affiliated Hospital of Chengdu Medical College, Sichuan, China.
Front Public Health. 2025 Mar 5;13:1523408. doi: 10.3389/fpubh.2025.1523408. eCollection 2025.
With the outbreak of Mpox in non-endemic countries in May 2022, which has captured international attention. In response, this study leveraged the real-time, predictive, and wide coverage advantages of big data to reflect the public's needs and interests regarding the Mpox epidemic, and explore its potential early warning role. We carried out a systematic data search weekly on two major network data platforms-Baidu Search Index (BDI) and WeChat Search Index (WCI) in China, and the index data overview, main concern information, hotspot regional distribution were analyzed. Besides, the correlation between the search index and the number of new cases of Mpox globally and within China were also investigated. Our results showed that both BDI and WCI mirrored the trends of the Mpox epidemic, with peaks in interest aligning with the release of relevant policies and events. The public's interest evolved from basic knowledge of the disease to a focus on treatment and prevention, with attentiveness centrally placed in economically developed areas such as Guangdong, Beijing, and Shanghai. A positive correlation was observed between the Chinese epidemic and the BDI ( = 0.372, = 0.047) and WCI ( = 0.398, = 0.044), whereas non-correlation was noted globally. Notably, when the search time was delayed by 1 week, both BDI and WCI showed a positive correlation with the epidemic in China and globally. Overall, the integrated use of big data offers a platform for rapid understanding public concerns and early warning signs of emerging infectious diseases such as Mpox.
随着2022年5月猴痘在非流行国家爆发，这一事件引起了国际关注。对此，本研究利用大数据的实时、预测和广泛覆盖优势，以反映公众对猴痘疫情的需求和兴趣，并探索其潜在的预警作用。我们每周在中国的两个主要网络数据平台——百度搜索指数（BDI）和微信搜索指数（WCI）上进行系统的数据搜索，并对指数数据概况、主要关注信息、热点地区分布进行了分析。此外，还研究了搜索指数与全球及中国国内猴痘新病例数之间的相关性。我们的结果表明，BDI和WCI都反映了猴痘疫情的趋势，兴趣高峰与相关政策和事件的发布相一致。公众的兴趣从对疾病的基本知识演变到对治疗和预防的关注，关注度集中在广东、北京和上海等经济发达地区。观察到中国疫情与BDI（ = 0.372， = 0.047）和WCI（ = 0.398， = 0.044）之间呈正相关，而在全球范围内未发现相关性。值得注意的是，当搜索时间延迟1周时，BDI和WCI在中国和全球范围内均与疫情呈正相关。总体而言，大数据的综合应用为快速了解公众关注以及猴痘等新兴传染病的预警信号提供了一个平台。
