Geographical information system based study on Dengue and Chikungunya at Western Ghats districts, Tamil Nadu, India

Authors: N. Bharathi, C.M. Ramakritinan, K. Kolandaswamy

Background & Objectives: Dengue and Chikungunya risk, based on socio-cultural and environmental factors and its possible spatial relationship to be investigated broadly. The aim of study is to explore epidemiology of risk factors of Dengue and Chikungunya prevalence and incidence, mapping the diseases prevalence in the year 2011for future planning containment measures in Western Ghats districts Dindigul HUD, Theni and Madurai.
Methods: Dengue and Chikungunya outbreak reports were obtained from sentinel diseases surveillance centers. Entomological parameters were applied to investigate and monitor fever outbreaks. Meteorological data was obtained from India Meteorological Department for risk factors analysis. ArcGIS modeling was done to generate risk map of Dengue and Chikungunya incidences with four risk levels i.e. very high, high, medium and low in three districts.
Results: 21 Dengue outbreaks, 13Chikungunya outbreaks, 3 mixed outbreaks in Madurai district, 24 Dengue outbreaks, one Chikungunya outbreak, 2 mixed outbreaks in Theni district and 13 Dengue, one Chikungunya and one mixed outbreaks at Dindigul HUD were recorded during 2011. Aedes albopictus and Aedes aegypti are common vectors in these three districts. The entomological variables are strongly correlated with prevalence of dengue and chikungunya outbreaks (r=0.91). Madurai district reported more Chikungunya outbreaks than other district. Seasonal rainfall maintained breeding sources and outbreaks in all months in Theni district. Interpretation & Conclusions: Results of this study indicated that socio-economic and socio-cultural variables are highly correlated with prevalence of Dengue and Chikungunya. This risk zone map helps in implementing precautionary and preventive strategies and control incidences of vector borne diseases effectively. Data about these diseases prevalence including location can be incorporated easily in GIS for comprehensive analyses.

 

 

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