Presenter : Dibyendu Adhikari
Malaria is a vector-borne disease caused by Plasmodium vivex and P. falciparum, parasitic protozoans, and is transmitted mainly by infected female Anopheles mosquito. The disease is most prevalent in the tropical and sub-tropical regions of the world. An estimated 214 million cases of malaria have been reported globally leading to an estimated deaths of 438,000, of which more than 90% of the deaths occurred in the developing and under-developed countries (WHO). It has been hypothesized that global warming would increase the occurrence of vector-borne diseases such as malaria. However, adequate evidences based on experiments or predictive models do not exist to support such hypothesis. The present paper therefore attempts to model the disease breakout in future based on the extent and distribution of the vectors under different climate change scenarios in India using ecological niche modeling as a tool. This would help in prioritizing the areas those are highly susceptible to the disease for adoption of appropriate preventive measures.
In the present study, free and open-source data and software tools were used to model the distribution of malaria vector in India under current as well as future climatic conditions. Global occurrence records for the vector species of genus Anopheles were obtained from www.vectorbase.org/. Bioclimatic data for the current conditions with a spatial resolution of 2.5 arc minutes were obtained from www.worldclim.org. Ecological niche models for the malaria vectors were generated based on the current climatic conditions using Maxent 3.3.3k, a freely available niche modeling software (www.cs.princeton.edu/~schapire/maxent/). For future predictions pertaining to the time frame of 2080, we used statistically downscaled Hadley climate models with representative concentration pathways (RCP) of 2.6 and 8.5 (http://ccafs-climate.org). RCPs are greenhouse gas concentration trajectories adopted by IPCC for its fifth Assessment Report, and describe the possible climate futures depending on how much greenhouse gases will be emitted in the years to come. For spatial data processing, analysis and visualization of the model outputs, we used open-source GIS softwares namely Q-GIS, DIVA, and NicheA 3.0. The niche models indicate a clear increase in geographical extent of the vector leading to elevated risk under future climate change scenarios. The study also demonstrates the importance of free and open source GIS tools in modelling the distribution of potentially harmful vectors of human diseases.