Geospatial analysis of malaria incidence in parts of Assam
Student name: Ms Lucy Humtsoe
Guide: Dr Anu Rani Sharma
Year of completion: 2013
Host Organisation: North Eastern Space Applications Centre (NESAC), Umiam
Supervisor (Host Organisation): Dr Bijoy Krishna Handique
Abstract: Malaria is considered as a key public health problem in all over the world. It is the
most important of all the tropical diseases in terms of morbidity and mortality. Malaria is
widespread in most part of the North Eastern States of India where Plasmodium falciparum is
regarded as the predominant Parasite. In this study the main aim was to map the distribution of
the malaria incidence and find out the relation between the malaria incidence and the vegetation
indices (NDVI and EVI), LST and meteorological data in five districts of Assam which was
selected randomly. Moran’s I tool which is a measure of degree of spatial autocorrelation was
used to check the distribution of the malaria incidence with its location in each district. Hotspot
analysis calculated by G statistics indicates how the malaria incidence has been distributed over
the districts. Through the Z score value for each of the Sub Centre, it was decided that a Z score
greater than 1.96 was labeled as ‘Hotspot’ class and below this value it was considered to be
‘Other’ class. By using interpolation technique (Universal kriging) a probability map was
generated where the high probability of malaria incidence location varies from one district to
another. Relation between the malaria incidence and NDVI, EVI, LST and rice crop area was
derived using regression method. Analysis of distribution of malaria incidence showed that there
was no significant spatial autocorrelation recorded in Sub Centres for Lakhimpur, Bongaigaon
and Morigaon district but a significant spatial autocorrelation was found in Sonitpur and
Tinsukia district. Four Sub Centres in Lakhimpur district was identified as Hotspots and three
Sub Centres in Tinsukia district was identified as Hotspots. Relation of malaria incidence and RS
derived parameters showed significant correlation between the malaria incidence and NDVI,
EVI where the R2 ranges from 0.61-0.8 but a significant relation could not be established
between malaria incidence and LST.
Key words: Malaria, Vegetation indices, Z score, Spatial autocorrelation, Sub Centre.
