Identification of social and bio-ecological changes on distribution of malaria vector in the Districts of Ampara and Batticaloa.

Abstract

Identification of social and bio-ecological changes on distribution of malaria vector in the Districts of Ampara and Batticaloa

Malaria is a widespread vector-borne disease in the world, endemic throughout the tropical and subtropical regions. In Sri Lanka, Ampara and Batticaloa Districts were considered as endemic malaria prevailing areas. This research was to incorporate Geographical Information System (GIS) to investigate the relationship between the vector densities with socio and bio-ecological changes in seven malaria sensitive areas (sentinel sites) in Districts of Ampara and Batticaloa. Each sentinel site contained four Grama Niladhari Divisions (GND) (localities) with a radius of 5-20 km. An in-depth locality and house-hold surveys were conducted to collect information on environment and house-holds. Entomologic l surveillance was conducted during the period from April, 2013 to July, 2014 for 15 months. All selected localties, households and entomological surveillance points were mapped. Data on mean temperature, total rainfall, wind speed and humidity were also collected and incoporated into GIS. Pearson's correlation coefficients (r), Arc GIS modeling and time series analysis were carried out to investigate the associations among anopheline densities with climatic variables, sociological and other parameters.

51000 anopheline mosquitoes were collected. Most abundant species were An. nigerrimus, An. peditaeniatus and An. subpictus. Out of which, An. subpictus showed endophilic behavior while An. nigerrimus appeared to be exophlic in nature. Maximum temperature with 2 months lag period and minimum temperature and rain fall of the current month were the most influential meteorological parameters affecting density of Anophelines. Further, use of Long Lasting Insecticide Nets (LLIN), household parameters (house, roof and wall types) and sociological parameters are affecting vector density. Arc GIS model developed showed majority of the houses were within the highly threatened area. Risk maps developed in the study areas revealed that the densities of vector mosquitoes are rising in Dehiattakndiya and Mahaoya (Ampara District) and Thirukkovil and Vakarei (Batticaloa Distict) areas. Despite the fact there had not been An. culicifacies found as well as Sri Lanka being certified as a malaria free country in 2016, prediction of the prevalence of malaria vector is important for a country like Sri Lanka as it was found that the densities of secondary vector still exist.

Key Words: Risk map, Model builder, Models, Prediction