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Optimal lead time for dengue forecast.

Hii YL, Rocklöv J, Wall S, Ng LC, Tang CS, Ng N - PLoS Negl Trop Dis (2012)

Bottom Line: 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.Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area.These lag times provided a forecast window of 1-5 months based on the observed weather data.

View Article: PubMed Central - PubMed

Affiliation: Umeå Centre for Global Health Research, Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. yienling.hii@epiph.umu.se

ABSTRACT

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.

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Related in: MedlinePlus

Effects of mean temperature and rain on dengue cases at various lag times.Upper panel shows relative risks of dengue cases as functions of weekly mean temperature and lower panel shows relative risks of dengue cases as functions of weekly cumulative rainfall at lag times of 4–20 weeks. Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
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pntd-0001848-g001: Effects of mean temperature and rain on dengue cases at various lag times.Upper panel shows relative risks of dengue cases as functions of weekly mean temperature and lower panel shows relative risks of dengue cases as functions of weekly cumulative rainfall at lag times of 4–20 weeks. Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.

Mentions: Our findings show that increasing weekly mean temperatures and cumulative rainfall precede risks of increasing dengue cases by 4–20 and 8–20 weeks, respectively (Figure 1). Each degree increase of mean temperature from 25.5°C–28.2°C elevated risk of dengue almost linearly at lag week 4–16 with peak at lag week 12; while an inverse relationship was observed between mean temperature and dengue cases at lag week 20. Figure 1 also shows mean temperature above 28.2°C raised the risk of dengue cases at lag weeks 4–20 with highest risk at lag week 16. Overall, the highest risk of dengue as a function of mean temperature occurred at lag week 12 followed by week 16. Simultaneously, each unit increase of weekly cumulative rainfall below 60 mm and above 150 mm elevated the risk of dengue cases at lag weeks 8–16 and weeks 12–20, respectively. Likewise, Table 2 shows each unit increase of weekly mean temperature raises higher incidence rate ratios for dengue cases at lag week 12 (IRR = 1.46) and week 16 (IRR = 1.39). Overall rate ratios for dengue cases in response to one mm rise of weekly cumulative rainfall peaked at lag week 16 (IRR = 1.011) and every unit increase of cumulative rainfall below 60 mm and above 150 mm elevated risks of dengue cases by 0.6% and 0.8%, respectively. Our model explained about 91% of the variance in dengue cases using weather predictors, time trend, and past cases. Residuals diagnoses and PACF indicated that the model was fit for analysis with predicted cases against observed cases as shown in Figure 2. Sensitivity tests using various degrees of freedom on the spline function of trend showed little change in risk functions.


Optimal lead time for dengue forecast.

Hii YL, Rocklöv J, Wall S, Ng LC, Tang CS, Ng N - PLoS Negl Trop Dis (2012)

Effects of mean temperature and rain on dengue cases at various lag times.Upper panel shows relative risks of dengue cases as functions of weekly mean temperature and lower panel shows relative risks of dengue cases as functions of weekly cumulative rainfall at lag times of 4–20 weeks. Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3475667&req=5

pntd-0001848-g001: Effects of mean temperature and rain on dengue cases at various lag times.Upper panel shows relative risks of dengue cases as functions of weekly mean temperature and lower panel shows relative risks of dengue cases as functions of weekly cumulative rainfall at lag times of 4–20 weeks. Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
Mentions: Our findings show that increasing weekly mean temperatures and cumulative rainfall precede risks of increasing dengue cases by 4–20 and 8–20 weeks, respectively (Figure 1). Each degree increase of mean temperature from 25.5°C–28.2°C elevated risk of dengue almost linearly at lag week 4–16 with peak at lag week 12; while an inverse relationship was observed between mean temperature and dengue cases at lag week 20. Figure 1 also shows mean temperature above 28.2°C raised the risk of dengue cases at lag weeks 4–20 with highest risk at lag week 16. Overall, the highest risk of dengue as a function of mean temperature occurred at lag week 12 followed by week 16. Simultaneously, each unit increase of weekly cumulative rainfall below 60 mm and above 150 mm elevated the risk of dengue cases at lag weeks 8–16 and weeks 12–20, respectively. Likewise, Table 2 shows each unit increase of weekly mean temperature raises higher incidence rate ratios for dengue cases at lag week 12 (IRR = 1.46) and week 16 (IRR = 1.39). Overall rate ratios for dengue cases in response to one mm rise of weekly cumulative rainfall peaked at lag week 16 (IRR = 1.011) and every unit increase of cumulative rainfall below 60 mm and above 150 mm elevated risks of dengue cases by 0.6% and 0.8%, respectively. Our model explained about 91% of the variance in dengue cases using weather predictors, time trend, and past cases. Residuals diagnoses and PACF indicated that the model was fit for analysis with predicted cases against observed cases as shown in Figure 2. Sensitivity tests using various degrees of freedom on the spline function of trend showed little change in risk functions.

Bottom Line: 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.Finally, we assessed the gap between forecast and successful control to determine the optimal timing for issuing an early warning in the study area.These lag times provided a forecast window of 1-5 months based on the observed weather data.

View Article: PubMed Central - PubMed

Affiliation: Umeå Centre for Global Health Research, Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. yienling.hii@epiph.umu.se

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus