Limits...
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 dynamics of the relative contributions of the variables for 121 BRT models (the gray rectangle indicates the period of the optimal lag time, i.e., 58 lag days to 62 lag days).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036700&req=5

ijerph-13-00867-f003: The dynamics of the relative contributions of the variables for 121 BRT models (the gray rectangle indicates the period of the optimal lag time, i.e., 58 lag days to 62 lag days).

Mentions: The dynamics of the relative contributions of different variables (Figure 3) showed that the relative contribution of the daily average temperature increased unsteadily from the lag time of 0 day to approximately 60 days. The relative contribution of daily average humidity remained stable most of the time, whereas the relative contributions of the other variables, including precipitation, sunshine duration, and wind speed, decreased to different levels with the increase in lag time from 0 day to 60 days.


Meteorological Factors for Dengue Fever Control and Prevention in South China
The dynamics of the relative contributions of the variables for 121 BRT models (the gray rectangle indicates the period of the optimal lag time, i.e., 58 lag days to 62 lag days).
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00867-f003: The dynamics of the relative contributions of the variables for 121 BRT models (the gray rectangle indicates the period of the optimal lag time, i.e., 58 lag days to 62 lag days).
Mentions: The dynamics of the relative contributions of different variables (Figure 3) showed that the relative contribution of the daily average temperature increased unsteadily from the lag time of 0 day to approximately 60 days. The relative contribution of daily average humidity remained stable most of the time, whereas the relative contributions of the other variables, including precipitation, sunshine duration, and wind speed, decreased to different levels with the increase in lag time from 0 day to 60 days.

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.