Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability
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...
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| creator | Sang, Shaowei Yin, Wenwu Bi, Peng Zhang, Honglong Wang, Chenggang Liu, Xiaobo Chen, Bin Yang, Weizhong Liu, Qiyong |
| description | 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.
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-R2, 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.
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. |
| doi_str_mv | 10.1371/journal.pone.0102755 |
| format | Article |
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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-R2, 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.
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0102755</identifier><identifier>PMID: 25019967</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aedes aegypti ; Airports ; Analysis ; Animals ; Aquatic insects ; China - epidemiology ; Climate ; Climate change ; Climate variability ; Climatic variability ; Collaboration ; Collinearity ; Correlation analysis ; Culicidae ; Culicidae - physiology ; Data processing ; Dengue ; Dengue - epidemiology ; Dengue - transmission ; Dengue fever ; Density ; Disease control ; Disease Outbreaks - prevention & control ; Disease prevention ; Disease transmission ; Early warning systems ; Emergency warning programs ; Epidemics ; Fever ; Forecasting ; General circulation models ; Geography ; Goodness of fit ; Health aspects ; Health care ; Humans ; Humidity ; Infectious diseases ; Laboratories ; Mathematical models ; Medical diagnosis ; Medicine and Health Sciences ; Mosquitoes ; Poisson density functions ; Population ; Population Density ; Precipitation (Meteorology) ; Predictive control ; Pressure ; Preventive medicine ; Principal Component Analysis ; Principal components analysis ; Rain ; Rainfall ; Regression analysis ; Relative humidity ; Risk analysis ; Risk Factors ; Seasonal distribution ; Statistical analysis ; Suburbs ; Temperature ; Vapor pressure ; Variability ; Vector-borne diseases ; Viral diseases ; Viruses ; Weather</subject><ispartof>PloS one, 2014-07, Vol.9 (7), p.e102755</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Sang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014 Sang et al 2014 Sang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-9e1692bcff17ef807eb4d4a7a8a31a7a3f3d03beb8dd29d712aa7f2d25fa87ac3</citedby><cites>FETCH-LOGICAL-c692t-9e1692bcff17ef807eb4d4a7a8a31a7a3f3d03beb8dd29d712aa7f2d25fa87ac3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097061/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097061/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25019967$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ooi, Eng Eong</contributor><creatorcontrib>Sang, Shaowei</creatorcontrib><creatorcontrib>Yin, Wenwu</creatorcontrib><creatorcontrib>Bi, Peng</creatorcontrib><creatorcontrib>Zhang, Honglong</creatorcontrib><creatorcontrib>Wang, Chenggang</creatorcontrib><creatorcontrib>Liu, Xiaobo</creatorcontrib><creatorcontrib>Chen, Bin</creatorcontrib><creatorcontrib>Yang, Weizhong</creatorcontrib><creatorcontrib>Liu, Qiyong</creatorcontrib><title>Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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-R2, 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.
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.</description><subject>Aedes aegypti</subject><subject>Airports</subject><subject>Analysis</subject><subject>Animals</subject><subject>Aquatic insects</subject><subject>China - epidemiology</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate variability</subject><subject>Climatic variability</subject><subject>Collaboration</subject><subject>Collinearity</subject><subject>Correlation analysis</subject><subject>Culicidae</subject><subject>Culicidae - physiology</subject><subject>Data processing</subject><subject>Dengue</subject><subject>Dengue - epidemiology</subject><subject>Dengue - transmission</subject><subject>Dengue fever</subject><subject>Density</subject><subject>Disease control</subject><subject>Disease Outbreaks - prevention & control</subject><subject>Disease prevention</subject><subject>Disease transmission</subject><subject>Early warning systems</subject><subject>Emergency warning programs</subject><subject>Epidemics</subject><subject>Fever</subject><subject>Forecasting</subject><subject>General circulation models</subject><subject>Geography</subject><subject>Goodness of fit</subject><subject>Health aspects</subject><subject>Health care</subject><subject>Humans</subject><subject>Humidity</subject><subject>Infectious diseases</subject><subject>Laboratories</subject><subject>Mathematical models</subject><subject>Medical diagnosis</subject><subject>Medicine and Health Sciences</subject><subject>Mosquitoes</subject><subject>Poisson density functions</subject><subject>Population</subject><subject>Population Density</subject><subject>Precipitation (Meteorology)</subject><subject>Predictive control</subject><subject>Pressure</subject><subject>Preventive medicine</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Regression analysis</subject><subject>Relative humidity</subject><subject>Risk analysis</subject><subject>Risk Factors</subject><subject>Seasonal distribution</subject><subject>Statistical analysis</subject><subject>Suburbs</subject><subject>Temperature</subject><subject>Vapor pressure</subject><subject>Variability</subject><subject>Vector-borne diseases</subject><subject>Viral diseases</subject><subject>Viruses</subject><subject>Weather</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk22L1DAQx4so3rn6DUQDgiDsrknTxzfCsei5cHDi09uQJtM2R5rsJenh-RX80mbd3rEFBemLKZPf_DP8M5MkzwleE1qSt1d2dIbr9c4aWGOC0zLPHySnpKbpqkgxfXj0f5I88f4K45xWRfE4OUlzTOq6KE-TX58cSCWCMh3SVnCNJJhuBBQcN35Q3itrkDLofOSm-9nbcYk2vTJ8iULv7Nj1MUIEWj2CEYBsi9Swsy6ARIJ78Es0WH89qmD30l6FW8RNPNNq4AHQDXeKN0rH_NPkUcu1h2dTXCTfPrz_uvm4urg8327OLlaiqNOwqoHE2Ii2JSW0FS6hyWTGS15xSmKgLZWYNtBUUqa1LEnKedmmMs1bXpVc0EXy8qC709azyUfPSJ7lGKcFqSKxPRDS8iu2c7FVd8ssV-xPwrqOcReU0MCyMqV1hWmWlyIrKtIAIYTKNou3YShk1Ho33TY2A0gBJlqrZ6LzE6N61tkbluG6xAWJAq8mAWevR_DhHy1PVMdjV_E9bBQT8QEFO8tIlecUx04XyfovVPwkDErESWpVzM8K3swKIhPgR-j46D3bfvn8_-zl9zn7-ojtgevQe6vHEMfNz8HsAApnvXfQ3jtHMNsvwp0bbL8IbFqEWPbi2PX7orvJp78BOacGTg</recordid><startdate>20140714</startdate><enddate>20140714</enddate><creator>Sang, 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Bin</au><au>Yang, Weizhong</au><au>Liu, Qiyong</au><au>Ooi, Eng Eong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-07-14</date><risdate>2014</risdate><volume>9</volume><issue>7</issue><spage>e102755</spage><pages>e102755-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.
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-R2, 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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25019967</pmid><doi>10.1371/journal.pone.0102755</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2014-07, Vol.9 (7), p.e102755 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1545002618 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Aedes aegypti Airports Analysis Animals Aquatic insects China - epidemiology Climate Climate change Climate variability Climatic variability Collaboration Collinearity Correlation analysis Culicidae Culicidae - physiology Data processing Dengue Dengue - epidemiology Dengue - transmission Dengue fever Density Disease control Disease Outbreaks - prevention & control Disease prevention Disease transmission Early warning systems Emergency warning programs Epidemics Fever Forecasting General circulation models Geography Goodness of fit Health aspects Health care Humans Humidity Infectious diseases Laboratories Mathematical models Medical diagnosis Medicine and Health Sciences Mosquitoes Poisson density functions Population Population Density Precipitation (Meteorology) Predictive control Pressure Preventive medicine Principal Component Analysis Principal components analysis Rain Rainfall Regression analysis Relative humidity Risk analysis Risk Factors Seasonal distribution Statistical analysis Suburbs Temperature Vapor pressure Variability Vector-borne diseases Viral diseases Viruses Weather |
| title | Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability |
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