Spatiotemporal distribution of dengue vectors & identification of high risk zones in district Sonitpur, Assam, India.

ABSTRACT

Background & objectives: Dengue is an arboviral disease of public health importance in many parts of India and recently many cases have been reported from northeastern India. Aedes mosquitoes, which are the vectors of dengue, are widely prevalent in the region. A study was initiated in Sonitpur district of Assam to understand the spatiotemporal distribution and seasonal prevalence of dengue vectors and to identify the high risk zones. Methods: Ovitrap surveys were conducted in three randomly selected villages under each of the eight public health centres (PHC) in district Sonitpur of Assam, northeastern India during March 2011 - February 2012. Three risk zones (high, medium and low) were identified on the basis of per trap density of Aedes mosquitoes. Meteorological data were collected to study the temporal distribution of dengue vectors. Results: Aedes albopictus (99.3%) was the predominant dengue vector followed by Ae. aegypti (0.7%) recorded in the ovitraps. The highest vector density was observed during the post-monsoon (60.1 ± 18 per trap) while the lowest during the winter (7.6 ± 4.9 per trap) and the season-wise differences in the vector density were significant (p=0.005). Maximum temperature (correlation coefficient, r = 0.45) and minimum temperature (r = 0.408) showed the highest positive correlation with the vector density, whereas the number of rainy days showed high positive correlation (r = 0.185) than the total rainfall (r = 0.117). The high risk zone (Dekhiajuli, Behali, Bihaguri and Gohpur PHC) as indicated by the high larval densities of dengue vectors, 45.3 ± 18, 42.1 ± 22.3, 36.9 ± 29.1, 35.3 ± 22.6 per trap, respectively, was validated by dengue epidemiological data collected during 2012. Interpretation & conclusions: Yearlong monitoring of dengue vectors was done for the first time in this region. Monthly maximum temperature and the number of rainy days could be used for the prediction of larval density of Aedes mosquitoes. The identification high dengue risk zones would help in adopting targeted interventions for disease management in future.