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Risk factors spatial-temporal detection for dengue fever in Guangzhou
Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518558/ https://www.ncbi.nlm.nih.gov/pubmed/30360767 http://dx.doi.org/10.1017/S0950268818002820 |
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author | Kong, Lingcai Xu, Chengdong Mu, Pengfei Li, Jialiang Qiu, Senyue Wu, Haixia |
author_facet | Kong, Lingcai Xu, Chengdong Mu, Pengfei Li, Jialiang Qiu, Senyue Wu, Haixia |
author_sort | Kong, Lingcai |
collection | PubMed |
description | Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38–0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies. |
format | Online Article Text |
id | pubmed-6518558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65185582019-06-04 Risk factors spatial-temporal detection for dengue fever in Guangzhou Kong, Lingcai Xu, Chengdong Mu, Pengfei Li, Jialiang Qiu, Senyue Wu, Haixia Epidemiol Infect Original Paper Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38–0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies. Cambridge University Press 2018-10-26 /pmc/articles/PMC6518558/ /pubmed/30360767 http://dx.doi.org/10.1017/S0950268818002820 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Kong, Lingcai Xu, Chengdong Mu, Pengfei Li, Jialiang Qiu, Senyue Wu, Haixia Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title | Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title_full | Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title_fullStr | Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title_full_unstemmed | Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title_short | Risk factors spatial-temporal detection for dengue fever in Guangzhou |
title_sort | risk factors spatial-temporal detection for dengue fever in guangzhou |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518558/ https://www.ncbi.nlm.nih.gov/pubmed/30360767 http://dx.doi.org/10.1017/S0950268818002820 |
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