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Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005–2014

dc.contributor.authorXiang, Jianjunen
dc.contributor.authorHansen, Alanaen
dc.contributor.authorLiu, Qiyongen
dc.contributor.authorLiu, Xiaoboen
dc.contributor.authorTong, Michael Xiaoliangen
dc.contributor.authorSun, Yehuanen
dc.contributor.authorCameron, Scotten
dc.contributor.authorHanson-Easey, Scotten
dc.contributor.authorHan, Gil Sooen
dc.contributor.authorWilliams, Craigen
dc.contributor.authorWeinstein, Philipen
dc.contributor.authorBi, Pengen
dc.date.accessioned2025-04-05T08:32:44Z
dc.date.available2025-04-05T08:32:44Z
dc.date.issued2017-02-01en
dc.description.abstractThis study aims to (1) investigate the associations between climatic factors and dengue; and (2) identify the susceptible subgroups. De-identified daily dengue cases in Guangzhou for 2005–2014 were obtained from the Chinese Center for Disease Control and Prevention. Weather data were downloaded from the China Meteorological Data Sharing Service System. Distributed lag non-linear models (DLNM) were used to graphically demonstrate the three-dimensional temperature-dengue association. Generalised estimating equation models (GEE) with piecewise linear spline functions were used to quantify the temperature-dengue associations. Threshold values were estimated using a broken-stick model. Middle-aged and older people, people undertaking household duties, retirees, and those unemployed were at high risk of dengue. Reversed U-shaped non-linear associations were found between ambient temperature, relative humidity, extreme wind velocity, and dengue. The optimal maximum temperature (Tmax) range for dengue transmission in Guangzhou was 21.6–32.9 °C, and 11.2–23.7 °C for minimum temperature (Tmin). A 1 °C increase of Tmax and Tmin within these ranges was associated with 11.9% and 9.9% increase in dengue at lag0, respectively. Although lag effects of temperature were observed for up to 141 days for Tmax and 150 days for Tmin, the maximum lag effects were observed at 32 days and 39 days respectively. Average relative humidity was negatively associated with dengue when it exceeded 78.9%. Maximum wind velocity (&$2gt;10.7 m/s) inhibited dengue transmission. Climatic factors had significant impacts on dengue in Guangzhou. Lag effects of temperature on dengue lasted the local whole epidemic season. To reduce the likely increasing dengue burden, more efforts are needed to strengthen the capacity building of public health systems.en
dc.description.sponsorshipThe research has been funded by the Australian Government Department of Foreign Affairs and Trade through the Australian Development Research Awards Scheme under an award titled ‘How best to curb the public health impact of emerging and re-emerging infectious diseases due to climate change in China’ ( Project ID: 66888 ). The views expressed in the publication are those of the author(s) and not necessarily those of the Department of Foreign Affairs and Trade or the Australian Government. The Commonwealth of Australia accepts no responsibility for any loss, damage or injury resulting from reliance on any of the information or views contained in this publication. We sincerely thank Chinese Center for Disease Control and Prevention for support in providing dengue data. The authors also acknowledge Dr Murthy Mittinty and Dr Yuanyuan Wang for their assistance in data analysis.en
dc.description.statustrueen
dc.format.extent10en
dc.identifier.otherresearchoutputwizard:a383154xPUB33811en
dc.identifier.otherScopus:84996995691en
dc.identifier.urihttps://dspace-test.anu.edu.au/handle/1885/733756697
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84996995691&partnerID=8YFLogxKen
dc.language.isoEnglishen
dc.rightsPublisher Copyright: © 2016 Elsevier Inc.en
dc.sourceEnvironmental Researchen
dc.subjectClimate changeen
dc.subjectDengue feveren
dc.subjectGuangzhouen
dc.subjectInfectious diseaseen
dc.subjectWeatheren
dc.titleAssociation between dengue fever incidence and meteorological factors in Guangzhou, China, 2005–2014en
dc.typeArticleen
local.bibliographicCitation.lastpage26en
local.bibliographicCitation.startpage17en
local.contributor.affiliationXiang, Jianjun; University of Adelaideen
local.contributor.affiliationHansen, Alana; University of Adelaideen
local.contributor.affiliationLiu, Qiyong; Chinese Center for Disease Control and Preventionen
local.contributor.affiliationLiu, Xiaobo; Chinese Center for Disease Control and Preventionen
local.contributor.affiliationTong, Michael Xiaoliang; University of Adelaideen
local.contributor.affiliationSun, Yehuan; Anhui Medical Universityen
local.contributor.affiliationCameron, Scott; University of Adelaideen
local.contributor.affiliationHanson-Easey, Scott; University of Adelaideen
local.contributor.affiliationHan, Gil Soo; Monash Universityen
local.contributor.affiliationWilliams, Craig; University of South Australiaen
local.contributor.affiliationWeinstein, Philip; University of Adelaideen
local.contributor.affiliationBi, Peng; University of Adelaideen
local.identifier.citationvolume153en
local.identifier.doi10.1016/j.envres.2016.11.009en
local.identifier.purebc4b1395-f56e-4212-a171-1b34a06b78b9en
local.type.statusPublisheden

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