Mathematical modeling of dengue dynamics and climate variability in Sri Lanka

Abstract

Dengue fever (DF) is a life threatening infectious mosquito borne disease that places a heavy burden on public health system in Sri Lanka as well as on most of the tropical countries around the world. Currently, there is no antiviral drug for treatment of DF. The objective of this study is twofold, first is to analyze the epidemic outbreak patterns of dengue cases in 25 districts in Sri Lanka, second is to identify the association between climatic variables and dengue counts in Colombo district where dengue is predominant. Weekly data on dengue cases were obtained between January, 2009 – September, 2014. Temperature (maximum, minimum, mean), precipitation, visibility, humidity, and wind speed were also recorded as weekly averages. Wavelet analyses were used to explore the periodicity of dengue cases. Wavelet coherence was performed to identify the association between dengue and climatic factors. Further, a Poisson regression combined with distributed lag nonlinear model (dlnm) was used to quantify the impact of climatic factors on dengue counts while taking the lag time into account. Change point analysis was performed as a complementary analytic method to identify changes in variance of dengue and climate time series. Dengue dynamics showed multiple periodic patterns (1-8 weeks, 26 weeks and 52 weeks) across twenty five districts which can be divided into two groups based on wavelet cluster analysis. Wavelet coherency revealed a significant non-stationary association between climatic variables and dengue incidence in annual and semi-annual scale. Results of dlnm revealed mean temperature around 250C – 260C prior to 5 weeks, high precipitation (>30mm), humidity 65% - 75% prior to lag of 10-15 weeks, and high visibility have an harmful impact on increasing relative risk of dengue incidence. These findings can aid the targeting of vector control interventions and planning for dengue vaccine implementation.