Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

Tu, Bizhi; Xiong, Cheng; Jia, Yaya; Qian, Jun

doi:10.1186/s12879-020-05740-x

Cited by 24 publications

(27 citation statements)

References 20 publications

(21 reference statements)

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“…As representatives of current Chinese search engines and social networking platforms, their internet data are more valuable for monitoring China's first national public crisis since it entered the mobile social media era. Since COVID-19, researchers have used media reports (25), Google Trends (26), Weibo post counts, Baidu searches (27,28), and Ali indices (29,30) to study public attention and risk communication for COVID-19 epidemic management. But there has been no study of the Chinese public's response to COVID-19 using data such as BDI and SMI from the perspective of supply and demand.…”

Section: Introductionmentioning

confidence: 99%

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Gong

Hou

Han

et al. 2022

Front. Public Health

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ObjectivesThe internet data is an essential tool for reflecting public attention to hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm correlation between COVID-19 case data and Chinese online data (public attention). This could verify the effect of online data on early warning of public health events, which will enable us to respond in a more timely and effective manner.MethodsSpearman correlation was used to check the consistency of BDI and SMI. Time lag cross-correlation analysis of BDI, SMI and six case-related indicators and multiple linear regression prediction were performed to explore the correlation between public concern and the actual epidemic.ResultsThe public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak. BDI, SMI and COVID-19 indicators had significant advance or lag effects, among which SMI and six indicators all had advance effects while BDI only had advance effects with new confirmed cases and new death cases. But compared with the SMI, the BDI was more closely related to the epidemic severity. Notably, the prediction model constructed by BDI and SMI can well fit new confirmed cases and new death cases.ConclusionsThe confirmed associations between the public's attention to the outbreak of COVID and the trend of epidemic outbreaks implied valuable insights into effective mechanisms of crisis response. In response to public health emergencies, people can through the information recommendation functions of social media and search engines (such as Weibo hot search and Baidu homepage recommendation) to raise awareness of available disease prevention and treatment, health services, and policy change.

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Section: Introductionmentioning

confidence: 99%

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Gong

Hou

Han

et al. 2022

Front. Public Health

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“…In this study, the novel coronavirus was used as the keyword, and the time span was from January 9, 2020 (when the pathogen was preliminarily determined to be COVID-19), to April 14, 2020 (when the ICU ward of Wuhan Jinyintan Hospital announced that they had ’zero COVID-19 patients’; a monumental victory in the control of COVID-19) [ 35 , 36 ]. Based on the Baidu index, the spatial and temporal differences of and factors that influence the novel coronavirus attention network were analysed, and the spreading trend and severity of the epidemic in various provinces in China were determined to scientifically and accurately assess the form of the national epidemic and provide a basis for promoting the precise prevention and control of the epidemic.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal characteristics of the novel coronavirus attention network and its influencing factors in China

Guo

2021

PLoS ONE

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The outbreak of a novel coronavirus pneumonia (COVID-19), wherein more than 200 million people have been infected and millions have died, poses a great threat to achieving the United Nations 2030 sustainable development goal (SDGs). Based on the Baidu index of ’novel coronavirus’, this paper analyses the spatial and temporal characteristics of and factors that influenced the attention network for COVID-19 from January 9, 2020, to April 15, 2020. The study found that (1) Temporally, the attention in the new coronavirus network showed an upward trend from January 9 to January 29, with the largest increase from January 23 to January 29 and a peak on January 29, and then a slow downward trend. The level of attention in the new coronavirus network was basically flat when comparing January 22 and March 4. (2) Spatially, first, from the perspective of regional differences, the network attention in the eastern and central regions decreased in turn. The network users in the eastern region exhibited the highest attention to the new coronavirus, especially in Guangdong, Shandong, Jiangsu and other provinces and cities. The network attention in Tibet, Xinjiang, Qinghai and Ningxia in the western region was the lowest in terms of the national network attention. Second, from the perspective of interprovincial differences, the attention in the new coronavirus network was highly consistent with the Hu Huanyong line of China’s population boundary. The east of the Hu Huanyong line is densely populated, and the network showed high concern, mostly ranking at the third to fifth levels. (3) The number of Internet users in the information technology field, the population, and the culture and age characteristics of individuals are important factors that influence the novel coronavirus attention network.

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“…Despite the huge medical expenditure imposed on China by TDs ( 14), due to China's vast territory and large population, it is di cult to evaluate the true prevalence rate of TDs and to understand the characteristics and needs of TDs patients. With the wide application of the internet and the increasing reliance of the public on search engines as the main way to query health information, some online digital diseases surveillance tools has been explored in recent years (15)(16)(17). As a query tool, search engine can provide sensitive information on the disease before the diagnosis of the disease is reported, thus improving disease control.…”

Section: Discussionmentioning

confidence: 99%

Chinese Interest in Thyroid Related Diseases: Evidence from Baidu Index

Fan

Mou

et al. 2021

Preprint

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Background: Common thyroid diseases are hyperthyroidism, hypothyroidism, thyroiditis, thyroid tumor and so on. Baidu is currently the most widely used online search tool in China, has developed an internet search trends collection and analysis tool called the Baidu Index. The aim of the present study was to understand the trend and characteristics of public’s online attention to thyroid diseases, and to explore the value of Baidu Index in monitoring online retrieval behavior of thyroid related information.Methods: Taking the period from January 1, 2011 to December 31, 2019 as the time range into consideration, we used the big data analysis tool of Baidu Index and took “thyroid nodules”, “thyroid cancer”, “thyroiditis” “hyperthyroidism” and “hypothyroidism” as the keywords, the data of “search index” and “media index” were recorded on a weekly basis, and all information were aggregated into quarterly and annual to generate the final data which was carried out for secondary analysis. Pearson correlation analysis was used to analyze the correlation between the search index of keywords and the year. One-way Analysis of Variance was used to analyze the differences between search index and media index.Results: Among the five keywords, thyroid nodule search index had the highest growth rate (640%), followed by thyroid cancer (298%). The media’s attention to thyroid diseases had been declining year by year. Unlike the public’s attention, the media index of hyperthyroidism was significantly higher than other keywords.Conclusion: Over the past nine years, the public's attention to thyroid related diseases has been increasing gradually. Baidu Index is an effective tool to track the health information query behavior of Chinese internet users, which can provide a cost-effective supplement to traditional monitoring system.

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Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

Cited by 24 publications

References 20 publications

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Spatio-temporal characteristics of the novel coronavirus attention network and its influencing factors in China

Chinese Interest in Thyroid Related Diseases: Evidence from Baidu Index

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