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Climate variability and increase in intensity and magnitude of dengue incidence in Singapore
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.ORCID iD: 0000-0003-4030-0449
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.ORCID iD: 0000-0003-0556-1483
Environment Health Department, National Environment Agency, Singapore.
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2009 (English)In: Global Health Action, ISSN 1654-9716, E-ISSN 1654-9880, Vol. 2, 124-132 p.Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: Dengue is currently a major public health burden in Asia Pacific Region. This study aims to establish an association between dengue incidence, mean temperature and precipitation, and further discuss how weather predictors influence the increase in intensity and magnitude of dengue in Singapore during the period 2000-2007.

MATERIALS AND METHODS: Weekly dengue incidence data, daily mean temperature and precipitation and the midyear population data in Singapore during 2000-2007 were retrieved and analysed. We employed a time series Poisson regression model including time factors such as time trends, lagged terms of weather predictors, considered autocorrelation, and accounted for changes in population size by offsetting.

RESULTS: The weekly mean temperature and cumulative precipitation were statistically significant related to the increases of dengue incidence in Singapore. Our findings showed that dengue incidence increased linearly at time lag of 5-16 and 5-20 weeks succeeding elevated temperature and precipitation, respectively. However, negative association occurred at lag week 17-20 with low weekly mean temperature as well as lag week 1-4 and 17-20 with low cumulative precipitation.

DISCUSSION: As Singapore experienced higher weekly mean temperature and cumulative precipitation in the years 2004-2007, our results signified hazardous impacts of climate factors on the increase in intensity and magnitude of dengue cases. The ongoing global climate change might potentially increase the burden of dengue fever infection in near future.

Place, publisher, year, edition, pages
CoAction Publishing, 2009. Vol. 2, 124-132 p.
Keyword [en]
dengue fever, Aedes aegypti, weather, mean temperature, precipitation, climate variability, incidence
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:umu:diva-30928DOI: 10.3402/gha.v2i0.2036ISI: 000208160000041PubMedID: 20052380OAI: oai:DiVA.org:umu-30928DiVA: diva2:288826
Available from: 2010-01-21 Created: 2010-01-21 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Climate and dengue fever: early warning based on temperature and rainfall
Open this publication in new window or tab >>Climate and dengue fever: early warning based on temperature and rainfall
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: Dengue is a viral infectious disease that is transmitted by mosquitoes. The disease causes a significant health burden in tropical countries, and has been a public health burden in Singapore for several decades. Severe complications such as hemorrhage can develop and lead to fatal outcomes. Before tetravalent vaccine and drugs are available, vector control is the key component to control dengue transmission. Vector control activities need to be guided by surveillance of outbreak and implement timely action to suppress dengue transmission and limit the risk of further spread. This study aims to explore the feasibility of developing a dengue early warning system using temperature and rainfall as main predictors. The objectives were to 1) analyze the relationship between dengue cases and weather predictors, 2) identify the optimal lead time required for a dengue early warning, 3) develop forecasting models, and 4) translate forecasts to dengue risk indices.

Methods: Poisson multivariate regression models were established to analyze relative risks of dengue corresponding to each unit change of weekly mean temperature and cumulative rainfall at lag of 1-20 weeks. Duration of vector control for localized outbreaks was analyzed to identify the time required by local authority to respond to an early warning. Then, dengue forecasting models were developed using Poisson multivariate regression. Autoregression, trend, and seasonality were considered in the models to account for risk factors other than temperature and rainfall. Model selection and validation were performed using various statistical methods. Forecast precision was analyzed using cross-validation, Receiver Operating Characteristics curve, and root mean square errors. Finally, forecasts were translated into stratified dengue risk indices in time series formats.

Results: Findings showed weekly mean temperature and cumulative rainfall preceded higher relative risk of dengue by 9-16 weeks and that a forecast with at least 3 months would provide sufficient time for mitigation in Singapore. Results showed possibility of predicting dengue cases 1-16 weeks using temperature and rainfall; whereas, consideration of autoregression and trend further enhance forecast precision. Sensitivity analysis showed the forecasting models could detect outbreak and non-outbreak at above 90% with less than 20% false positive. Forecasts were translated into stratified dengue risk indices using color codes and indices ranging from 1-10 in calendar or time sequence formats. Simplified risk indices interpreted forecast according to annual alert and outbreak thresholds; thus, provided uniform interpretation.

Significance: A prediction model was developed that forecasted a prognosis of dengue up to 16 weeks in advance with sufficient accuracy. Such a prognosis can be used as an early warning to enhance evidence-based decision making and effective use of public health resources as well as improved effectiveness of dengue surveillance and control. Simple and clear dengue risk indices improve communications to stakeholders.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2013. 61 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1554
Keyword
dengue fever, temperature, rainfall, forecasting model, early warning, epidemic, dengue risk index
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Public health
Identifiers
urn:nbn:se:umu:diva-68040 (URN)978-91-7459-589-5 (ISBN)
Public defence
2013-05-03, Sal 135, Allmänmedicin, Norrlands Universitetssjukhus, Umeå, 13:00 (English)
Opponent
Supervisors
Funder
FAS, Swedish Council for Working Life and Social Research, Grant No. 2006-1512
Available from: 2013-04-12 Created: 2013-04-11 Last updated: 2015-04-29Bibliographically approved

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