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. 2015 Sep 24:4:42.
doi: 10.1186/s40249-015-0075-8.

A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003 -2012) and lessons learned

N D B Ehelepola  1 , Kusalika Ariyaratne  2 , W M N P Buddhadasa  3 , Sunil Ratnayake  4 , Malani Wickramasinghe  5
Affiliations

A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003 -2012) and lessons learned

N D B Ehelepola et al. Infect Dis Poverty. .

Abstract

Background: Weather variables affect dengue transmission. This study aimed to identify a dengue weather correlation pattern in Kandy, Sri Lanka, compare the results with results of similar studies, and establish ways for better control and prevention of dengue.

Method: We collected data on reported dengue cases in Kandy and mid-year population data from 2003 to 2012, and calculated weekly incidences. We obtained daily weather data from two weather stations and converted it into weekly data. We studied correlation patterns between dengue incidence and weather variables using the wavelet time series analysis, and then calculated cross-correlation coefficients to find magnitudes of correlations.

Results: We found a positive correlation between dengue incidence and rainfall in millimeters, the number of rainy and wet days, the minimum temperature, and the night and daytime, as well as average, humidity, mostly with a five- to seven-week lag. Additionally, we found correlations between dengue incidence and maximum and average temperatures, hours of sunshine, and wind, with longer lag periods. Dengue incidences showed a negative correlation with wind run.

Conclusion: Our results showed that rainfall, temperature, humidity, hours of sunshine, and wind are correlated with local dengue incidence. We have suggested ways to improve dengue management routines and to control it in these times of global warming. We also noticed that the results of dengue weather correlation studies can vary depending on the data analysis.

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Figures

Fig. 1
Fig. 1
Variations of weekly dengue incidence (per 100,000 population) during the course of 52 weeks of each year, 2003–2012
Fig. 2
Fig. 2
Wavelet analysis results for the maximum temperature versus dengue incidence time series as a sample (2a–2f). a Weekly average maximum temperature (x-axis: year, y-axis: weekly average maximum temperature); b) Cross-wavelet transform (XWT) (x-axis: year, y-axis: period in years); c) XWT power for each period (x-axis: power, y-axis: period in years); d) Wavelet coherence (WTC) (x-axis: year, y-axis: period in years); e) WTC power for each period (x-axis: power, y-axis: period in years); f) The time series relevant to maximum wavelet coherence is reconstructed, and shown in this figure. In Figures 2b and d, there are color codes on the right side of the main figure. These indicate the magnitude of XWT and WTC; dark blue and dark red indicate lowest and highest magnitudes respectively. The thin parabolic black line demarcates the cone of influence
See this image and copyright information in PMC

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