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Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

INTRODUCTION: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explor...

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Autores principales: Sang, Shaowei, Yin, Wenwu, Bi, Peng, Zhang, Honglong, Wang, Chenggang, Liu, Xiaobo, Chen, Bin, Yang, Weizhong, Liu, Qiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097061/
https://www.ncbi.nlm.nih.gov/pubmed/25019967
http://dx.doi.org/10.1371/journal.pone.0102755
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author Sang, Shaowei
Yin, Wenwu
Bi, Peng
Zhang, Honglong
Wang, Chenggang
Liu, Xiaobo
Chen, Bin
Yang, Weizhong
Liu, Qiyong
author_facet Sang, Shaowei
Yin, Wenwu
Bi, Peng
Zhang, Honglong
Wang, Chenggang
Liu, Xiaobo
Chen, Bin
Yang, Weizhong
Liu, Qiyong
author_sort Sang, Shaowei
collection PubMed
description INTRODUCTION: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. METHODOLOGY AND PRINCIPAL FINDINGS: Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R(2), Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. CONCLUSIONS: Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.
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spelling pubmed-40970612014-07-17 Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability Sang, Shaowei Yin, Wenwu Bi, Peng Zhang, Honglong Wang, Chenggang Liu, Xiaobo Chen, Bin Yang, Weizhong Liu, Qiyong PLoS One Research Article INTRODUCTION: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. METHODOLOGY AND PRINCIPAL FINDINGS: Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R(2), Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. CONCLUSIONS: Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China. Public Library of Science 2014-07-14 /pmc/articles/PMC4097061/ /pubmed/25019967 http://dx.doi.org/10.1371/journal.pone.0102755 Text en © 2014 Sang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sang, Shaowei
Yin, Wenwu
Bi, Peng
Zhang, Honglong
Wang, Chenggang
Liu, Xiaobo
Chen, Bin
Yang, Weizhong
Liu, Qiyong
Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title_full Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title_fullStr Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title_full_unstemmed Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title_short Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability
title_sort predicting local dengue transmission in guangzhou, china, through the influence of imported cases, mosquito density and climate variability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097061/
https://www.ncbi.nlm.nih.gov/pubmed/25019967
http://dx.doi.org/10.1371/journal.pone.0102755
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