Using Baidu Search Engine to Monitor AIDS Epidemics Inform for Targeted intervention of HIV/AIDS in China

Li, Kang; Liu, Meiliang; Feng, Yi; Ning, Chuanyi; Ou, Weidong; Sun, Jia; Wei, Wudi; Liang, Hao; Shao, Yiming

doi:10.1038/s41598-018-35685-w

Cited by 38 publications

(36 citation statements)

References 40 publications

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“…Google search data have already been applied in the monitoring and predicting of new diagnoses of HIV infection in USA at state level 22 23. In China there are also articles using Baidu search data to predict the incidence of HIV/AIDS 24–26. These studies made an inspiration for adopting more attempts, such as multivariate time series analysis, to explore more effective predictions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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ObjectivesInternet search engine data have been widely used to monitor and predict infectious diseases. Existing studies have found correlations between search data and HIV/AIDS epidemics. We aimed to extend the literature through exploring the feasibility of using search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China.MethodsThis paper used vector autoregressive model to combine the number of newly diagnosed cases with Baidu search index to predict monthly newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China. The procedures included: (1) keywords selection and filtering; (2) construction of composite search index; (3) modelling with training data from January 2011 to October 2016 and calculating the prediction performance with validation data from November 2016 to October 2017.ResultsThe analysis showed that there was a close correlation between the monthly number of newly diagnosed cases and the composite search index (the Spearman’s rank correlation coefficients were 0.777 for HIV/AIDS, 0.590 for syphilis and 0.633 for gonorrhoea, p<0.05 for all). The R2were all more than 85% and the mean absolute percentage errors were less than 11%, showing the good fitting effect and prediction performance of vector autoregressive model in this field.ConclusionsOur study indicated the potential feasibility of using Baidu search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The Autoregressive Integrated Moving Average (ARIMA) model is a widely used model that can use the past and the present to predict the future 13 . The ARIMA model has been applied to predict other diseases epidemics such as AIDS 14 .…”

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Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

Zhao

Yang

et al. 2020

Sci Rep

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Reporting on brucellosis, a relatively rare infectious disease caused by Brucella, is often delayed or incomplete in traditional disease surveillance systems in china. internet search engine data related to brucellosis can provide an economical and efficient complement to a conventional surveillance system because people tend to seek brucellosis-related health information from Baidu, the largest search engine in china. in this study, brucellosis incidence data reported by the cDc of china and Baidu index data were gathered to evaluate the relationship between them. We applied an autoregressive integrated moving average (ARiMA) model and an ARiMA model with Baidu search index data as the external variable (ARiMAX) to predict the incidence of brucellosis. the two models based on brucellosis incidence data were then compared, and the ARiMAX model performed better in all the measurements we applied. our results illustrate that Baidu index data can enhance the traditional surveillance system to monitor and predict brucellosis epidemics in china.

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“…This may have an advantage especially for detecting outbreaks in China considering the population of WeChat users. Previous studies used search query data, including Baidu Index, to monitor the activity of emerging infectious diseases [15][16][17]. As far as we know, this is the first time to use WeChat data in this field.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

WeChat, a Chinese social media, may early detect the SARS-CoV-2 outbreak in 2019

Wang

Zhang

et al. 2020

Preprint

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Using Baidu Search Engine to Monitor AIDS Epidemics Inform for Targeted intervention of HIV/AIDS in China

Cited by 38 publications

References 40 publications

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

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

Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

WeChat, a Chinese social media, may early detect the SARS-CoV-2 outbreak in 2019

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