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Meteorological Factors for Dengue Fever Control and Prevention in South China

View Article: PubMed Central - PubMed

ABSTRACT

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005–2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

No MeSH data available.


The AUC values for 121 BRT models.
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ijerph-13-00867-f002: The AUC values for 121 BRT models.

Mentions: The AUC values of 120 lag models, alongside the model with no lag time, are plotted in Figure 2. Two high local AUC peaks for the 58 and 62 lag days could be identified. The AUC values for the training and cross-validation data increased with the increase in lag time from 1 day to approximately 60 days and decreased gradually afterward. The dynamics of different lag models and the two specific lag models that attained the highest AUC among the two datasets were analyzed in more detail. A comparison between the predicted values (lag-62 model) and the actual case series (in the absence/presence data) is presented in AppendixFigure A1.


Meteorological Factors for Dengue Fever Control and Prevention in South China
The AUC values for 121 BRT models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036700&req=5

ijerph-13-00867-f002: The AUC values for 121 BRT models.
Mentions: The AUC values of 120 lag models, alongside the model with no lag time, are plotted in Figure 2. Two high local AUC peaks for the 58 and 62 lag days could be identified. The AUC values for the training and cross-validation data increased with the increase in lag time from 1 day to approximately 60 days and decreased gradually afterward. The dynamics of different lag models and the two specific lag models that attained the highest AUC among the two datasets were analyzed in more detail. A comparison between the predicted values (lag-62 model) and the actual case series (in the absence/presence data) is presented in AppendixFigure A1.

View Article: PubMed Central - PubMed

ABSTRACT

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005–2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

No MeSH data available.