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 – , Bihar, India India Geospatial Forum, 8 th 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 BIHAR

Data Used  Ground Survey Data – i. Disease Incidence Report ii. 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 Survey Satellite data Topographical Sheet Pre-processing Spectral Analysis SAVI LST Entomological Data Climatic Data WI Statistical Analysis Epidemiological Data Integration in GIS Platform Geo-statistical Analysis Model Development for sandfly Prediction LULC Estimation of Association

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 PercentSergentomiyaPercentP. papatasiPercent Male (M) Female (F) Total %148100%15100% Relative abundance (%) M:F Ratio 1:1.591:1.181:1.50 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 ( ) < Room temperature1.42 (2.25 – 0.62) Room relative humidity (RH) 0.84 (1.13 – 0.56) < R 2 = 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-statisticp-value Intercept Minimum SAVI Mean SAVI Mean LST Minimum WI Maximum WI R 2 = 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 Variables Presence of LULC classes Chi-square test (X 2 ) P-value Settlement Marshy land Moist fallow River Sand Surface waterbody Vegetation Agricultural/crop land

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).