Public concerns analysis and early warning of Mpox based on network data platforms—taking Baidu and WeChat as example

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 (<i>r</i> = 0.372, <i>p</i> = 0.047) and WCI (<i>r</i> = 0.398, <i>p</i> = 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.

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Publication Details

Journal
Frontiers in Public Health
Published
2025-03-05
DOI
https://doi.org/10.3389/fpubh.2025.1523408
Citations
5
Primary Topic
Poxvirus research and outbreaks
Type
article
Field-Weighted Citation Impact
5.18
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article

Public concerns analysis and early warning of Mpox based on network data platforms—taking Baidu and WeChat as example

Xiaoyuan Hu, Jun Xia, Linlin Yu, Jiali Deng et al.
5 citations
Frontiers in Public Health
Poxvirus research and outbreaks
5.18
article

Public concerns analysis and early warning of Mpox based on network data platforms—taking Baidu and WeChat as example

Xiaoyuan Hu, Jun Xia, Linlin Yu, Jiali Deng, Song Fan, Wei Yu, Kai Yang, Bin Wang
article en
5 citations

Abstract

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 (<i>r</i> = 0.372, <i>p</i> = 0.047) and WCI (<i>r</i> = 0.398, <i>p</i> = 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.

Frontiers in Public HealthVol. 13
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Xinjiang Uygur Autonomous Region Disease Prevention and Control Center (CN), First Affiliated Hospital of Chengdu Medical College (CN), Chengdu Center for Disease Control and Prevention (CN)
Good health and well-being
Openalex Percentile: Top 5%
Poxvirus research and outbreaks
5.18
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