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- Publisher Website: 10.1136/bmjopen-2017-018335
- Scopus: eid_2-s2.0-85055079245
- PMID: 30337302
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Article: Using internet search data to predict new HIV diagnoses in China: a modelling study
Title | Using internet search data to predict new HIV diagnoses in China: a modelling study |
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Authors | |
Keywords | health informatics internet predictive model search query surveillance |
Issue Date | 2018 |
Publisher | BMJ Publishing Group: BMJ Open. The Journal's web site is located at http://bmjopen.bmj.com |
Citation | BMJ Open, 2018, v. 8 n. 10, p. article no. e018335 How to Cite? |
Abstract | Objectives Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.
Design We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).
Results Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.
Conclusions Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention. |
Persistent Identifier | http://hdl.handle.net/10722/278629 |
ISSN | 2023 Impact Factor: 2.4 2023 SCImago Journal Rankings: 0.971 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Q | - |
dc.contributor.author | CHAI, Y | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | Young, SD | - |
dc.contributor.author | Zhou, J | - |
dc.date.accessioned | 2019-10-21T02:11:08Z | - |
dc.date.available | 2019-10-21T02:11:08Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | BMJ Open, 2018, v. 8 n. 10, p. article no. e018335 | - |
dc.identifier.issn | 2044-6055 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278629 | - |
dc.description.abstract | Objectives Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China. Design We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016). Results Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks. Conclusions Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention. | - |
dc.language | eng | - |
dc.publisher | BMJ Publishing Group: BMJ Open. The Journal's web site is located at http://bmjopen.bmj.com | - |
dc.relation.ispartof | BMJ Open | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | health informatics | - |
dc.subject | internet | - |
dc.subject | predictive model | - |
dc.subject | search query | - |
dc.subject | surveillance | - |
dc.title | Using internet search data to predict new HIV diagnoses in China: a modelling study | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1136/bmjopen-2017-018335 | - |
dc.identifier.pmid | 30337302 | - |
dc.identifier.pmcid | PMC6196849 | - |
dc.identifier.scopus | eid_2-s2.0-85055079245 | - |
dc.identifier.hkuros | 307934 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | article no. e018335 | - |
dc.identifier.epage | article no. e018335 | - |
dc.identifier.isi | WOS:000454739500004 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2044-6055 | - |