Using Baidu search index to monitor and predict newly diagnosed cases of HIV/AIDS, syphilis and gonorrhea in China: estimates from a vector autoregressive (VAR) model

Huang, Ruonan; Luo, Ganfeng; Duan, Qibin; Zhang, Lei; Zhang, Qingpeng; Tang, Weiming; Smith, M. Kumi; Li, Jinghua; Zou, Huachun

doi:10.1136/bmjopen-2019-036098

Cited by 21 publications

(19 citation statements)

References 26 publications

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“…This study found that OMC and OMS were the Granger causes of confirmed cases during the outbreak of COVID-19, whereas OMA was not. The predictive power of OMS was not surprising, as it is in line with the Google Flu studies ( Dugas, Jalalpour, Gel et al., 2013 ; Martin, Lee, Yasui, 2016 ) and other Baidu search index studies ( He, Chen, Chen et al., 2018 ; Huang et al., 2020 ). However, the predictive value of OMS in this study was lower than that of OMC – an underexplored online medical behavior.…”

Section: Discussionsupporting

confidence: 80%

“…In present study, we applied the VAR model for multivariate time series analysis to capture their dynamic interdependence by taking each variable as the linear function of past lags of itself and the past lags of the other explanatory variables ( Hamilton, 1994 ; Lütkepohl, 2006 ). Recent studies used VAR models to predict the trend of infection diseases such as sexually transmitted diseases ( Huang, Luo, Duan et al., 2020 ), Dengue ( Ramadona, Lazuardi, Hii et al., 2016 ) and the ongoing COVID-19 ( Khan, Saeed, Ali et al., 2020 ; Khan, Saeed, Ali et al., 2020 ; Fantazzini, 2020 ). A basic VAR model contains a set of n endogenous variables y t = ( y 1 t , y 2 t , …, y n t ).…”

Section: Methodsmentioning

confidence: 99%

“…Comparatively, the rapidly growing online medical services, such as online medical consultation (OMC) and appointments (OMA), tend to provide more directly correlated data for pandemic prediction. Moreover, existing studies have often relied on a single source of data for prediction ( Husnayain, Fuad, & Su, 2020 ; Huang, Luo, Duan et al., 2020 ) and few studies have compared the predicative power of different sources ( Santillana, Nguyen, Dredze et al., 2015 ) for disease surveillance to identify the most effective and efficient approach for disease prediction. This study thus aims to focusing on investigating the online medical behavioral data for prediction and comparing the predicative power of varied online medical behaviors for pandemic surveillance.…”

Section: Literature Reviewmentioning

confidence: 99%

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Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Cao

Yang

Luo³

et al. 2021

Information Processing & Management

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Section: Discussionsupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Cao

Yang

Luo³

et al. 2021

Information Processing & Management

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“…Gu et al [ 30 ] found that the erythromelalgia epidemic search index showed the uptrend about a week ahead of the official report because of the delayed reports from the local Center for Disease Control and Prevention. Future research can establish relevant models, including the vector autoregressive model, to predict the future trend of the epidemic based on past values of the real-world data [ 22 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index

Gong¹,

Han²,

Hou³

et al. 2020

JMIR Public Health Surveill

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Background The COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies. Objective This study aims to use online search data (Baidu Index) to monitor the public’s attention and verify internet search engines’ function in public attention monitoring of public health emergencies. Methods We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention’s temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis. Results The Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases. Conclusions The national public’s demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public’s attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic.

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“…Previous studies mainly used daily new cases for time-delay correlation [16,22,[27][28][29][30][31][32][33], while this study used new cases and confirmed cases. For confirmed, death, and cured discharge cases, new variables are the number of cases that increased in 1 day, and cumulative variables are the superposition of all the case data up to that day.…”

Section: Bdi Has Significant Temporal Difference With Real-world Datamentioning

confidence: 99%

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index (Preprint)

Gong¹,

Han²,

Hou³

et al. 2020

Preprint

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BACKGROUND The COVID-19 pandemic has become a major global public health event, attracting worldwide public attention. As a tool to monitor public awareness, the internet search engine has been widely used in public health emergencies. OBJECTIVE This study aimed to use online search data (Baidu Index) to monitor the public's attention and verify internet search engines' function in public attention monitoring of public health emergencies. METHODS We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined Baidu Index of keywords related to COVID-19 to describe public attention's temporal trend and spatial distribution and conducted the time lag cross-correlation analysis. RESULTS Baidu Index temporal trend indicated that the changes of Baidu Index had a clear correspondence with the development time node of pandemic. Baidu Index spatial distribution showed that in the regions of central and eastern China with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. Also, Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases, while 3-5 days later than the change of three cumulative cases. CONCLUSIONS The national public's demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public's attention to the epidemic situation in other areas may lag behind. We could set Baidu Index as the sentinel and the database in the online surveillance system for infectious disease and public health emergencies. According to the monitoring data, the government need to prevent and control the possible outbreak in advance and communicate the risks to the public, so as to ensure the physical and psychological health of the public in the epidemic.

show abstract

Using Baidu search index to monitor and predict newly diagnosed cases of HIV/AIDS, syphilis and gonorrhea in China: estimates from a vector autoregressive (VAR) model

Cited by 21 publications

References 26 publications

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index (Preprint)

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