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Climate and dengue fever: early warning based on temperature and rainfall
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
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 [en]
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: urn:nbn:se:umu:diva-68040ISBN: 978-91-7459-589-5 (print)OAI: oai:DiVA.org:umu-68040DiVA: diva2:615782
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
List of papers
1. Climate variability and increase in intensity and magnitude of dengue incidence in Singapore
Open this publication in new window or tab >>Climate variability and increase in intensity and magnitude of dengue incidence in 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
Keyword
dengue fever, Aedes aegypti, weather, mean temperature, precipitation, climate variability, incidence
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-30928 (URN)10.3402/gha.v2i0.2036 (DOI)000208160000041 ()20052380 (PubMedID)
Available from: 2010-01-21 Created: 2010-01-21 Last updated: 2017-12-12Bibliographically approved
2. Optimal lead time for dengue forecast
Open this publication in new window or tab >>Optimal lead time for dengue forecast
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2012 (English)In: PLoS Neglected Tropical Diseases, ISSN 1935-2727, E-ISSN 1935-2735, Vol. 6, no 10, e1848- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: A dengue early warning system aims to prevent a dengue outbreak by providing an accurate prediction of a rise in dengue cases and sufficient time to allow timely decisions and preventive measures to be taken by local authorities. This study seeks to identify the optimal lead time for warning of dengue cases in Singapore given the duration required by a local authority to curb an outbreak.

METHODOLOGY AND FINDINGS: We developed a Poisson regression model to analyze relative risks of dengue cases as functions of weekly mean temperature and cumulative rainfall with lag times of 1-5 months using spline functions. We examined the duration of vector control and cluster management in dengue clusters > = 10 cases from 2000 to 2010 and used the information as an indicative window of the time required to mitigate an outbreak. Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area. Our findings show that increasing weekly mean temperature and cumulative rainfall precede risks of increasing dengue cases by 4-20 and 8-20 weeks, respectively. These lag times provided a forecast window of 1-5 months based on the observed weather data. Based on previous vector control operations, the time needed to curb dengue outbreaks ranged from 1-3 months with a median duration of 2 months. Thus, a dengue early warning forecast given 3 months ahead of the onset of a probable epidemic would give local authorities sufficient time to mitigate an outbreak.

CONCLUSIONS: Optimal timing of a dengue forecast increases the functional value of an early warning system and enhances cost-effectiveness of vector control operations in response to forecasted risks. We emphasize the importance of considering the forecast-mitigation gaps in respective study areas when developing a dengue forecasting model.

National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-61924 (URN)10.1371/journal.pntd.0001848 (DOI)000310527200015 ()23110242 (PubMedID)
Available from: 2012-12-03 Created: 2012-12-03 Last updated: 2017-12-07Bibliographically approved
3. Forecast of dengue incidence using temperature and rainfall
Open this publication in new window or tab >>Forecast of dengue incidence using temperature and rainfall
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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.

National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-62094 (URN)10.1371/journal.pntd.0001908 (DOI)000311888900036 ()23209852 (PubMedID)
Available from: 2012-12-07 Created: 2012-12-07 Last updated: 2017-12-07Bibliographically approved
4. Dengue risk index as an early warning
Open this publication in new window or tab >>Dengue risk index as an early warning
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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.

National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Public health
Identifiers
urn:nbn:se:umu:diva-68014 (URN)
Available from: 2013-04-11 Created: 2013-04-10 Last updated: 2015-04-29Bibliographically approved

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