GIS Integrated Epidemiological Indices for Risk Area Identification towards Malaria Control Measures
Abdul Qayum1, Andrew Michael Lynn2, Rakesh Arya3, Sanjay K Jaiswal4
1Abdul Qayum, Center for Biology & Bioinformatics, School of Computaional & Integrative Scienes, JNU, New Delhi, India.
2Andrew M Lynn, Associate Professor, Center for Biology & Bioinformatics, School of Computational & Integrative Sciences, JNU, New Delhi, India.
3Rakesh Arya, Center for the Study Regional Development, JNU, New Delhi, India.
4Dr. Sanjay K Jaiswal, 3Medical Officer, CHC Seovarhi Kushinagar, (U.P), India.
Manuscript received on July 12, 2013. | Revised Manuscript received on August 25, 2013. | Manuscript published on August 30, 2013. | PP: 377-381 | Volume-2, Issue-6, August 2013. | Retrieval Number: F2107082613/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The exponential increase in the mosquito born diseases has been found in the recent past. It is primarily because of the development of drug resistance of malarial parasites. It has various other reasons including indiscriminate use of pesticides, excessive deforestation and demographic shifts which are responsible for this enhanced rate of spreading of this epidemic. The current paper demonstrates a case study and an example of application of GIS integrated epidemiological indices for risk area identification. The main aim of the work is to identify the risk areas priority in the selected region of Eastern Uttar Pradesh (UP), India especially for Gorakhpur, Kushinagar & Maharajganj district region and to assimilate the results obtained from both GIS based and epidemiology. Computerised spatial database and GIS mapping software provides powerful tool for management and analysis of malaria control program. It proves to be a breakthrough towards various control measures. Using ArcGIS; maps were produced and assimilated to malarial hotspots. Further, various epidemiological indices like ABER, API, SPR, SFR were studied to understand malaria epidemicity of eastern UP and aimed to look for any possible bridge between these epidemiological indices.
Keywords: API, ABER, Epidemic, Epidemiology, GIS, Malaria, Mapping, PHC, WHO.