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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
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 predi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202716/ https://www.ncbi.nlm.nih.gov/pubmed/32209633 http://dx.doi.org/10.1136/bmjopen-2019-036098 |
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author | Huang, Ruonan Luo, Ganfeng Duan, Qibin Zhang, Lei Zhang, Qingpeng Tang, Weiming Smith, M. Kumi Li, Jinghua Zou, Huachun |
author_facet | Huang, Ruonan Luo, Ganfeng Duan, Qibin Zhang, Lei Zhang, Qingpeng Tang, Weiming Smith, M. Kumi Li, Jinghua Zou, Huachun |
author_sort | Huang, Ruonan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7202716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-72027162020-05-13 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 BMJ Open Epidemiology 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. BMJ Publishing Group 2020-03-23 /pmc/articles/PMC7202716/ /pubmed/32209633 http://dx.doi.org/10.1136/bmjopen-2019-036098 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Epidemiology Huang, Ruonan Luo, Ganfeng Duan, Qibin Zhang, Lei Zhang, Qingpeng Tang, Weiming Smith, M. Kumi Li, Jinghua Zou, Huachun 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 |
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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202716/ https://www.ncbi.nlm.nih.gov/pubmed/32209633 http://dx.doi.org/10.1136/bmjopen-2019-036098 |
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