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Dengue risk index as an early warning
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, Epidemiology and Global Health.
Environmental Health Institute, National Environment Agency, Singapore.
Department of Mathematics & Statistics, York University, Toronto, Canada.
Show others and affiliations
2013 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Introduction: A dengue early warning forewarns stakeholders and promotes timely prevention. Besides accuracy and timeliness, an effective early warning system must be comprised of a structure that allows clear and comprehensible communications to stakeholders, and facilitates planning of actions that corroborate with risks.  To aid such communication and planning efforts, this study established a risk-stratified forecast strategy which relies on uniformly interpreted risk indices derived from forecasted dengue cases.      

Methodologies & Findings: We adopted the Poisson forecasting model developed by Hii et al. (2012) as model-1 and established a model-2 that considered only temperature and rainfall. We validate and compared the models for their forecast precision and sensitivity to diagnose outbreak and non-outbreak. Models were trained using data from 2001-2010. Forecast precision for the period 2011-2012 was analyzed using six cross-validations of 16-weeks forecast and root mean square errors. Operating Characteristic curve was used to analyze sensitivity of models. Forecasts were then translated into dengue risk indices according to estimated alert and epidemic thresholds. 

Results showed that model-1 and model-2 explained about 84% and 70% of variance in dengue distribution, respectively. Average RMSE was 28 for model-1 and 33 for model-2 during cross-validations. ROC area was 0.96 (CI=0.93-0.98) for model-1 and 0.92 (CI=0.88-0.96) for model-2 in 2004-2010. The two models were able to forecast outbreak about 90% accuracy with around 10% false positive in 2011-2012.  Monthly and seasonal calendar risk index and weekly time series risk index were established using color scheme to represent risk levels.     

Significance: Translation of a forecast to dengue risk index permits rapid and clear interpretation of forecast; thus enhances the effectiveness of an early warning. Further studies on feasibility of developing an automated forecast-control-calibration-system using different forecasting methods to allow parallel forecast for comparison and monitoring will enhance sustainability of forecast precision.

Place, publisher, year, edition, pages
2013.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Public health
Identifiers
URN: urn:nbn:se:umu:diva-68014OAI: oai:DiVA.org:umu-68014DiVA: diva2:615568
Available from: 2013-04-11 Created: 2013-04-10 Last updated: 2015-04-29Bibliographically 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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Citation style
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