Identifying Potential Norovirus Epidemics in China via Internet Surveillance

Liu, Kui; Huang, Sichao; Miao, Ziping; Chen, Bin; Jiang, Tao; Cai, Guanqiang; Jiang, Zhenggang; Chen, Yongdi; Wang, Zhengting; Gu, Hongtao; Chai, Chengliang; Jiang, Jianmin

doi:10.2196/jmir.7855

Cited by 17 publications

(13 citation statements)

References 32 publications

(41 reference statements)

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“…It has been proven that various infectious diseases, including influenza,11–14 dengue15–18 and H7N9,19 can be effectively monitored and predicted by models using internet data. The sensitivity and specificity of search engine data-based monitoring or predicted results is on par with that of the traditional surveillance system 11 20. It is invaluable to extend this innovative method to other diseases, including HIV/AIDS, syphilis and gonorrhoea that lead to significant morbidity, mortality, clinical outcomes, and with no existing vaccines 21…”

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 information generated by the Baidu search data could be applied to epidemiological research. In Zhejiang Province of China, the prevalence of Norwalk virus has been determined through Internet monitoring (Baidu Index), and an appropriate model has been established to predict potential Norwalk virus infection 19 . Similarly, some methods of using Baidu Search Index(BSI) data have also been carried out in HIV/AIDS; hand, foot and mouth disease; and dengue 17,20,21 .…”

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confidence: 99%

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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“…Baidu search engine is the most widely accepted search engine in China, making it the most representative and available data source for the studies targeting tracking the online seeking behavior of Chinese people. Based on Baidu search query data, Chinese scholars have made great efforts in the field of disease monitoring, such as Norovirus ( Liu et al, 2017b ), Dengue ( Li et al, 2017 ), Hand, foot, and mouth disease ( Du et al, 2017 ), and epidemic erythromelalgia (EM) ( Gu et al, 2015 ). These forecasting models got great performances in the field of early warning.…”

Section: Discussionmentioning

confidence: 99%

Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

Liang

Guan

et al. 2018

PeerJ

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BackgroundInfluenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data.MethodsThe official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error.ResultsIn total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data.ConclusionsThe results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.

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Identifying Potential Norovirus Epidemics in China via Internet Surveillance

Cited by 17 publications

References 32 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

Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

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