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Remote Sensing and GIS: Tools for the Prediction of Epidemic for the Intervention Measure. Gouri Sankar Bhunia Department Of Vector Biology & Control, Rajendra Memorial Research Institute Of Medical Sciences (ICMR), Agamkuan , Patna – 800 007, Bihar, India. - PowerPoint PPT Presentation
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Remote Sensing and GIS: Tools for the Prediction of Epidemic for the
Intervention Measure
Gouri Sankar BhuniaDepartment Of Vector Biology & Control, Rajendra Memorial Research Institute Of Medical Sciences (ICMR), Agamkuan,
Patna – 800 007, Bihar, India
India Geospatial Forum, 8th February, 2011
What is ‘Kala-azar’? Kala-azar/Visceral leishmaniasis - vector-borne diseases
- protozoan parasite (Leishmania donovani)- Ph. argentipes
VL exists in 88 countries on five continents
Estimated 300,000 cases occurred annually and more than 60% of the global burden account for India, Nepal, and Bangladesh
Bihar contributes to over 80-85% of National Kala-Azar Burden.
32 Dist. Out of 38 dist. have endemic foci for Kala-Azar cases.
Disease affects mostly poorest of the poor specially Mushar Community.
Role of RS in Kala-azar Control
• RS technology - tool for the surveillance of habitats, densities of vector species and even prediction of the incidence of diseases.
• To recover continuous fields of air temperature, humidity, and vapor pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications.
• Role of RS by its synoptic coverage, high repetivity, bird eye view, inaccessibility, cost effectiveness
Role of GIS in Kala-azar Control
Seamlessly integrates disparate types of information sources Environmental Conditions Substance Characteristics Fate/Transport Exposure/Latency
Supports multidisciplinary analysis using a systems approach Environmental Economic Demographic
GIS architecture is ideal for handling the complexities of a large number of spatially distributed variables
Provides a vehicle for improving our understanding of the contextual relationships between factors
Objectives
Examining disease distribution and its relation with the environmental factors and vector distribution in a Kala-azar endemic region in Bihar, India
Study Area
BIHARBIHAR
Data Used Ground Survey Data –
i. Disease Incidence Report 2005-2010ii. Adult sandflies (Ph. argentipes) were collected
from indoors (Human dwelling & cattle shed) using CDC light trap
iii. Indoor climate (e.g., Temperature and Relative humidity)
Satellite Data –
Landsat-5 TM (Path/Row-141/42; DOP-22/10/09)
Methodology
Field SurveyField
SurveySatellite
dataSatellite
dataTopographical
SheetTopographical
Sheet
Pre-processing
Pre-processing
Spectral AnalysisSpectral Analysis
SAVISAVI
LSTLST
Entomological Data
Entomological Data
Climatic Data
Climatic Data
WIWI
Statistical
Analysis
Epidemiological Data
Epidemiological Data
Integration in GIS PlatformIntegration in GIS Platform
Geo-statistical Analysis
Geo-statistical Analysis
Model Development for sandfly Prediction
Model Development for sandfly Prediction
LULCLULC
Esti
mati
on
of
Associa
tion
Esti
mati
on
of
Associa
tion
RESULTS
Temporal distribution of cases and deaths of the study area
Calculation of mean centre and directional distribution of disease
Monthly distribution of sandfly density in the study site
P. argentipes
Percent Sergentomiya Percent P. papatasi Percent
Male (M) 126 36.95 80 54.05 9 60.00
Female (F) 215 63.05 68 45.95 6 40.00
Total 341 100% 148 100% 15 100%
Relative abundance (%)
67.66 29.37 2.98
M:F Ratio 1:1.59 1:1.18 1:1.50
Sandfly characteristics of the study area
Sandfly prediction based on inside room climate data
Predictor
Variables
Coefficients
(95% CI)
SE(βs) T-statistic
p-value
Intercept -87.26 (-108.88 - -66.33) 10.09 -8.65 <0.0000
Room temperature 1.42 (2.25 – 0.62) 0.39 3.69 0.0012
Room relative humidity (RH)
0.84 (1.13 – 0.56) 0.13 6.19 <0.0000
R2 = 0.80; p-value <0.0000
Relationship between maximum, minimum and mean SAVI value with sandfly density
Wetness Index (WI) map of the Muzaffarpur district
Land Surface Temperature (LST) of the study area, derived from Landsat- 5 TM
Prediction of sandfly based environmental variables derived from remote sensing
technology
Predictor
Variables
Standard Error
(βs) T-statistic p-value
Intercept 6.283 -1.746 0.006
Minimum SAVI 1.986 0.175 2.02 0.050Mean SAVI 3.284 0.228 2.636 0.015
Mean LST 0.198 0.251 1.965 0.013
Minimum WI 0.052 0.423 4.282 0.000
Maximum WI 0.041 0.301 2.825 0.010
R2 = 0.85; p-value<0.001
Land use/land cover (LULC) map of the study area
Association between land use/land cover classes with the presence/absence of
sandfly
VariablesPresence of LULC
classesChi-square test
(X2)P-value
Settlement 10 9.44 0.002Marshy land 16 3.92 0.048Moist fallow 23 0.75 0.384River 2 18.47 0.000Sand 3 6.63 0.010Surface waterbody 16 9.93 0.002Vegetation 9 4.33 0.040Agricultural/crop land
24 0.00 1.00
Discussion and Conclusion
Maximum number of sandfly species are recorded in the month of September-October, whereas, minimum number recorded from the month of January-February
Standard deviation of ellipse shows that disease are distributed from eastern to western direction
Inside room temperature and humidity play an important role for the breeding and propagation of sandfly distribution
The predictive value of this remote sensing map based on LST, SAVI and WI indices data appears to be better for the forecast of the disease risk areas.
Multivariate regression analysis showed that minimum SAVI, Mean SAVI, mean LST, minimum WI, maximum WI highly significant to predict the sandfly density.
Analysis of land use/land cover features revealed that adult sand fly density was significantly associated with land cover variables (e.g., settlement, surface water body, moist fallow, vegetation, sand and river).