About: 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

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About: 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 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	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
Creator	Zhang, Lei Tang, Weiming Li, Jinghua Smith, M Zhang, Qingpeng Duan, Qibin Huang, Ruonan Luo, Ganfeng Zou, Huachun
Source	Medline; PMC
abstract	OBJECTIVES: Internet 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. METHODS: This 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. RESULTS: The 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 R(2) were 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. CONCLUSIONS: Our 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.
has issue date	2020-03-23(xsd:dateTime)
bibo:doi	10.1136/bmjopen-2019-036098
bibo:pmid	32209633
has license	cc-by-nc
sha1sum (hex)	f588632170254ef1d67d5e63a431c279eb4a9c92
schema:url	https://doi.org/10.1136/bmjopen-2019-036098
resource representing a document's title	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
has PubMed Central identifier	PMC7202716
has PubMed identifier	32209633
schema:publication	BMJ Open
resource representing a document's body	covid:f588632170254ef1d67d5e63a431c279eb4a9c92#body_text
is schema:about of	named entity 'estimates' named entity 'model' named entity 'China' named entity 'HIV/AIDS' named entity 'p<0.05' named entity 'syphilis' named entity 'syphilis' named entity 'HIV' named entity 'infectious diseases' named entity 'gonorrhoea' named entity 'syphilis' named entity 'quality of life' named entity 'HIV' named entity 'time series analysis' named entity 'vector autoregressive model' named entity 'syphilis' named entity 'Baidu' named entity 'Portmanteau test' named entity 'severe acute respiratory syndrome' named entity 'vector' named entity 'HIV' named entity 'syphilis' named entity 'HIV/AIDS' named entity 'syphilis' named entity 'search engine' named entity 'syphilis' named entity 'epidemics' named entity 'AIDS' named entity 'heteroscedasticity' named entity 'p<0.05' named entity 'gonorrhoea' named entity 'China' named entity 'Baidu' named entity 'VAR' named entity 'VAR' named entity 'gonorrhoea' named entity 'routes of transmission' named entity 'Open access' named entity 'social media' named entity 'gonorrhea' named entity 'HIV/AIDS' named entity 'search engine' named entity 'China' named entity 'gonorrhoea' named entity 'HIV/AIDS' named entity 'infection' named entity 'regression techniques' named entity 'semantic' named entity 'autoregressive' named entity 'sexually transmitted diseases' named entity 'AIDS' named entity 'error correction model' named entity 'gonorrhoea' named entity 't-statistic' named entity 'Google' named entity 'linear function' named entity 'HIV/AIDS' named entity 'Google' named entity 'syphilis' named entity 'STDs' named entity 'Baidu' named entity 'gonorrhoea' named entity 'cointegration' named entity 'Baidu' named entity 'HIV/AIDS' named entity 'syphilis' named entity 'RMSE' named entity 'symptom' named entity 'co-integration' named entity 'STDs' named entity 'HIV/AIDS' named entity 'gonorrhoea' named entity 'symptom'

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