Assessing and Monitoring Malaria Epidemiology using Remote Sensing and GIS in Murshidabad District, West Bengal (India)
Poly Patra
Department of Geography, Seacom Skills University, Kendradangal, Santiniketan, Birbhum - 731236, West Bengal, India
Anukul Ch. Mandal
Department of Geography, Seacom Skills University, Kendradangal, Santiniketan, Birbhum - 731236, West Bengal, India
Raja Majumder
Department of Geography, Seacom Skills University, Kendradangal, Santiniketan, Birbhum - 731236, West Bengal, India
Debashish Ghosh
Department of Geography, Seacom Skills University, Kendradangal, Santiniketan, Birbhum - 731236, West Bengal, India
Gouri Sankar Bhunia *
Department of Geography, Seacom Skills University, Kendradangal, Santiniketan, Birbhum - 731236, West Bengal, India
*Author to whom correspondence should be addressed.
Abstract
A retrospective analysis of malaria cases was investigated at the block level in Murshidabad district between 2009 and 2016 to apprehend the trend and dynamics of transmission. A personal geodatabase was prepared in ArcGIS environment. The local spatial auto-correlation was investigated using Local Moran’s I statistics. The local Getis-Ord G statistics was used to estimate spatial clustering pattern of malaria. The maximum annual malaria incidence rate was recorded as 6.05/ 10,000 individuals in 2009 whereas, the low incidence rate was recorded in 2016. The occurrences of Plasmodium falciparum (P. falciparum) malaria were typically 3 ~ 5 times lower than those of P. vivax malaria incidence. The results also illustrated that the central part of the district was highly affected by the disease. The Moran’s I values for P. falciparum malaria were remarkably fluctuant and generally higher than those P. vivax malaria. The statistically significant high-low clustering pattern were observed for both the malaria cases in 2012 and 2013. Spatial cluster of P. vivax and P. falciparum malaria rehabilitated with time. However, this study suggests that appropriate countermeasures should target high threat areas accordingly and the undelaying sources of increased risk in the recognized areas.
Keywords: Malaria incidence, Moran’s I, Getis-Ord G statistics, malaria control