Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China.
BACKGROUND:Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study...
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ftdoajarticles:oai:doaj.org/article:9dc28fb7bce94b07930fa306c0f9a535 2023-05-15T15:08:12+02:00 Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. Zhihao Li Tao Liu Guanghu Zhu Hualiang Lin Yonghui Zhang Jianfeng He Aiping Deng Zhiqiang Peng Jianpeng Xiao Shannon Rutherford Runsheng Xie Weilin Zeng Xing Li Wenjun Ma 2017-03-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0005354 https://doaj.org/article/9dc28fb7bce94b07930fa306c0f9a535 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5354435?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005354 https://doaj.org/article/9dc28fb7bce94b07930fa306c0f9a535 PLoS Neglected Tropical Diseases, Vol 11, Iss 3, p e0005354 (2017) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2017 ftdoajarticles https://doi.org/10.1371/journal.pntd.0005354 2022-12-31T11:55:29Z BACKGROUND:Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. METHODOLOGY AND PRINCIPAL FINDINGS:A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). CONCLUSIONS:Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) PLOS Neglected Tropical Diseases 11 3 e0005354 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Zhihao Li Tao Liu Guanghu Zhu Hualiang Lin Yonghui Zhang Jianfeng He Aiping Deng Zhiqiang Peng Jianpeng Xiao Shannon Rutherford Runsheng Xie Weilin Zeng Xing Li Wenjun Ma Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND:Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. METHODOLOGY AND PRINCIPAL FINDINGS:A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). CONCLUSIONS:Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. |
format |
Article in Journal/Newspaper |
author |
Zhihao Li Tao Liu Guanghu Zhu Hualiang Lin Yonghui Zhang Jianfeng He Aiping Deng Zhiqiang Peng Jianpeng Xiao Shannon Rutherford Runsheng Xie Weilin Zeng Xing Li Wenjun Ma |
author_facet |
Zhihao Li Tao Liu Guanghu Zhu Hualiang Lin Yonghui Zhang Jianfeng He Aiping Deng Zhiqiang Peng Jianpeng Xiao Shannon Rutherford Runsheng Xie Weilin Zeng Xing Li Wenjun Ma |
author_sort |
Zhihao Li |
title |
Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
title_short |
Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
title_full |
Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
title_fullStr |
Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
title_full_unstemmed |
Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. |
title_sort |
dengue baidu search index data can improve the prediction of local dengue epidemic: a case study in guangzhou, china. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2017 |
url |
https://doi.org/10.1371/journal.pntd.0005354 https://doaj.org/article/9dc28fb7bce94b07930fa306c0f9a535 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Arctic Gam |
geographic_facet |
Arctic Gam |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 11, Iss 3, p e0005354 (2017) |
op_relation |
http://europepmc.org/articles/PMC5354435?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005354 https://doaj.org/article/9dc28fb7bce94b07930fa306c0f9a535 |
op_doi |
https://doi.org/10.1371/journal.pntd.0005354 |
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PLOS Neglected Tropical Diseases |
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11 |
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3 |
container_start_page |
e0005354 |
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