measures in the watershed. The above model has been used for
the present investigation to prioritise the Kala-Azar risk villages
using geo —environmental and climatic variables. Based on the
fuzzy membership assigned by expert in the field of
epidemiology, Fuzzy gamma operator 1; is calculated using
fuzzy algebraic product and algebraic sum with Y- factor which
produce the output coverage with maximum of fuzzy algebraic
sum and minimum of fuzzy algebraic product. This approach is
not only useful for predicting vector density but also for
prioritising the villages for effective control measures.
A software package for modelling the risk villages integrating
multivariate regression and fuzzy membership analysis models
have been developed using .NET environment and OPEN
Source GIS and Image Processing tools (Dotspatial,
Freeimage) to estimate MHD (vector density) as part of the
early warning system. The software package has an integrated
GIS and Image Viewer with specific Kala-Azar Vector Density
prediction and Fuzzy based Kala-Azar Risk assessment
modules. These models have been applied successfully through
the software package in highly endemic Vaishali district, India
and vector density have been calculated with good accuracy and
correlated with Kala-azar disease incidence in the district. Risk
modelling of villages and Early Warning System developed in
coordination with Rajendra Memorial research Institute of
Medical Sciences, Patna, India provided predictive measures of
MHD-vector density in different villages and different seasons
with reasonably good accuracy and maximize the surveillance
and control strategy.
1.4 Results and Discussions
Kala-azar risk villages in Vaishali district of Bihar have been
predicted based on multivariate regression models and fuzzy
based GIS model. It is observed higher degree of vector density
have been estimated in the villages where wetlands are
associated with good vegetation and higher humidity. MHD is
more in villages of mixed dwellings with mud walls and
thatched roofs. It is also observed that monthly mean maximum
temperature below 37.8°C and monthly mean minimum
temperature above 7.2?C, a mean annual relative humidity of
70% or more with a level not falling below 80% for at least
three months, an annual rainfall of 1250 mm or more with
favourable altitude below 600 m, soft stem peri-domestic
vegetation (banana), alluvial soil, high subsoil water level and
abundant vegetation as the most favourable factors [10]. All the
above ecological conditions prevail in most part of the northern
and central region of Bihar and highly suitable for abundance of
sandflies in these regions facilitating perennial transmission of
kala-azar.
P.argentipes prefers to rest in indoors, about 8-10 times higher
in cattle dwellings than in human dwellings. The most favoured
resting sites for sandflies include soil cracks, crevices inside the
human dwellings and cattlesheds. The species is predominantly
endophagic as evident from their higher indoor collections.
Temperature, Humidity, man-hour-density of sandfly,
peridomestic vegetation was collected from field based
observations. Soil samples collected from fields were also
analyzed for its constituents. Landuse parameters and NDVI
values were derived from satellite images. The results of the
ground - sampling survey and the village wise statistical point
data extracted from the landuse and NDVI data were analyzed
using an indigenously developed software module developed
for linear multivariate regression analysis. Using significantly
correlated variables with MHD, stepwise multivariate linear
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
regression analysis was carried out to establish the relationship
between predictor variables affecting vector density. The
variables (temperature, humidity, dry fallow, min. NDVI) were
found to be the best predictor of vector density, as analyzed by
linear regression. The regression model indicate that measured
and predicted MHD shows close agreement and R? value
calculated as 0.78. This indicates that model has predicted
MHD reasonably well. In Gurhi, Inta and Kujji villages have
indicated higher MHD based on the geo-environmental
parameters (Fig. 6).
Observed vs Predicted MHD
MHD
O — Ww OQ & O O - 0
B Observed MHD 8 Predicted MHD
Fig. 6 Graph showing comparison of observed and predicted
MHD
Fig.7 Village locations (a) Observed MHD and (b) predicted
MHD overlaid on IRS satellite data of Patepur Block,
Vaishali District.
The calculated MHD using the regression model and fuzzy
based GIS model is overlaid on the satellite data of Patepur
Block, Vaishali District which indicates the prevalence of
higher MHD in village locations where more wetlands and good
vegetation exists (Fig. 7).
The Standalone Software package (developed using Open
source GIS and Image Processing tools) is successfully
customised for Kala-Azar Risk modelling which has been used
for deriving the predicted MHD. For each point of interest (in
this case settlement locations) Landuse, NDVI and other climate
predictors are extracted in its proximity to determine the
suitable conditions required for the high vector density. Thus
the predicted MHD is calculated using multivariate regression
equation. The Software package has easy to use interface,
supporting various data formats (GIS, Image, Non —spatial data
format) and custom modules like Data creation / Editing,
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