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Forecast of dengue incidence using temperature and rainfall
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.ORCID iD: 0000-0003-0556-1483
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2012 (English)In: PLoS Neglected Tropical Diseases, ISSN 1935-2727, E-ISSN 1935-2735, Vol. 6, no 11, e1908- p.Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore.

METHODOLOGY AND PRINCIPAL FINDINGS: We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm.

SIGNIFICANCE: We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.

Place, publisher, year, edition, pages
2012. Vol. 6, no 11, e1908- p.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
URN: urn:nbn:se:umu:diva-62094DOI: 10.1371/journal.pntd.0001908ISI: 000311888900036PubMedID: 23209852OAI: diva2:575150
Available from: 2012-12-07 Created: 2012-12-07 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.
Umeå University medical dissertations, ISSN 0346-6612 ; 1554
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
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)
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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