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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

Duration of dengue clusters > = 10 cases with corresponding number of cases (2000–2010).X-axis represents strata of cluster-associated dengue cases and y-axis shows estimated time for cluster duration. Labels on y-axis denote: 1 = 1 month and less, 2 = exceeding 1 month with maximum 2 months, 3 = more than 2 months with maximum 3 months. Each bar in both panels represents percent of dengue clusters> = 10 that require corresponding duration (y-axis) for cluster management given strata of dengue cases (x-axis) in respective clusters. Left panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in non-epidemic years. Right panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in epidemic years (2004, 2005, and 2007).
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pntd-0001848-g005: Duration of dengue clusters > = 10 cases with corresponding number of cases (2000–2010).X-axis represents strata of cluster-associated dengue cases and y-axis shows estimated time for cluster duration. Labels on y-axis denote: 1 = 1 month and less, 2 = exceeding 1 month with maximum 2 months, 3 = more than 2 months with maximum 3 months. Each bar in both panels represents percent of dengue clusters> = 10 that require corresponding duration (y-axis) for cluster management given strata of dengue cases (x-axis) in respective clusters. Left panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in non-epidemic years. Right panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in epidemic years (2004, 2005, and 2007).

Mentions: In the past two decades, the total number of cluster-associated dengue cases contributed an average 27% of overall reported dengue cases for 1990–1999 and 30% for 2000–2010 (Figure 3). Since 2000, the proportion of cluster-associated dengue cases had been on an upward trend with a peak of 47% in year 2007. Whereas, dengue clusters that reported a minimum of 10 cases represented approximately a third of the total cluster-associated cases. From 2000–2010, the mean and median numbers of cases per cluster (> = 10 cases) were 22 and 17; mean and median numbers of cases were 21 and 16 for non-epidemic years and 23 and 19 for epidemic years (2004, 2005, and 2007), respectively (Figure 4). As shown in Figure 4, all the dengue clusters of 30 or less cases fell in the 75th percentile, except in years 2002 and 2005 when only 70% of clusters had fewer than this number. The differences in time required for dengue cluster control between non-epidemic and epidemic years is minor. Figure 5 indicates most of the dengue clusters have fewer than 30 cases and take up to 2 months to control dengue outbreaks in both non-epidemic and epidemic years. During the study period, approximately 23% (non-epidemic = 28%, epidemic = 16%) of the dengue clusters were managed within 1 month, 64% (non-epidemic = 60%, epidemic = 71%) was managed within 2 months, and 13% (non-epidemic = 13%, epidemic = 13%) required maximum 3 months of vector control and cluster management to curb outbreaks (Figure 5). Longer duration (2–3 months) for cluster management was required as the number of dengue cases in each cluster exceeded 36 and 30 for non-epidemic and epidemic years, respectively. Overall, the mean and median cluster duration was about 2 months for both non-epidemic and epidemic years.


Optimal lead time for dengue forecast.

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

Duration of dengue clusters > = 10 cases with corresponding number of cases (2000–2010).X-axis represents strata of cluster-associated dengue cases and y-axis shows estimated time for cluster duration. Labels on y-axis denote: 1 = 1 month and less, 2 = exceeding 1 month with maximum 2 months, 3 = more than 2 months with maximum 3 months. Each bar in both panels represents percent of dengue clusters> = 10 that require corresponding duration (y-axis) for cluster management given strata of dengue cases (x-axis) in respective clusters. Left panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in non-epidemic years. Right panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in epidemic years (2004, 2005, and 2007).
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0001848-g005: Duration of dengue clusters > = 10 cases with corresponding number of cases (2000–2010).X-axis represents strata of cluster-associated dengue cases and y-axis shows estimated time for cluster duration. Labels on y-axis denote: 1 = 1 month and less, 2 = exceeding 1 month with maximum 2 months, 3 = more than 2 months with maximum 3 months. Each bar in both panels represents percent of dengue clusters> = 10 that require corresponding duration (y-axis) for cluster management given strata of dengue cases (x-axis) in respective clusters. Left panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in non-epidemic years. Right panel shows percentage distribution of dengue clusters> = 10 corresponding to cluster duration and cases in epidemic years (2004, 2005, and 2007).
Mentions: In the past two decades, the total number of cluster-associated dengue cases contributed an average 27% of overall reported dengue cases for 1990–1999 and 30% for 2000–2010 (Figure 3). Since 2000, the proportion of cluster-associated dengue cases had been on an upward trend with a peak of 47% in year 2007. Whereas, dengue clusters that reported a minimum of 10 cases represented approximately a third of the total cluster-associated cases. From 2000–2010, the mean and median numbers of cases per cluster (> = 10 cases) were 22 and 17; mean and median numbers of cases were 21 and 16 for non-epidemic years and 23 and 19 for epidemic years (2004, 2005, and 2007), respectively (Figure 4). As shown in Figure 4, all the dengue clusters of 30 or less cases fell in the 75th percentile, except in years 2002 and 2005 when only 70% of clusters had fewer than this number. The differences in time required for dengue cluster control between non-epidemic and epidemic years is minor. Figure 5 indicates most of the dengue clusters have fewer than 30 cases and take up to 2 months to control dengue outbreaks in both non-epidemic and epidemic years. During the study period, approximately 23% (non-epidemic = 28%, epidemic = 16%) of the dengue clusters were managed within 1 month, 64% (non-epidemic = 60%, epidemic = 71%) was managed within 2 months, and 13% (non-epidemic = 13%, epidemic = 13%) required maximum 3 months of vector control and cluster management to curb outbreaks (Figure 5). Longer duration (2–3 months) for cluster management was required as the number of dengue cases in each cluster exceeded 36 and 30 for non-epidemic and epidemic years, respectively. Overall, the mean and median cluster duration was about 2 months for both non-epidemic and epidemic years.

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