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Remote Sensing and GIS: Tools for the Prediction of Epidemic for the Intervention Measure

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.

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Remote Sensing and GIS: Tools for the Prediction of Epidemic for the Intervention Measure

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  1. Remote Sensing and GIS: Tools for the Prediction of Epidemic for the Intervention Measure GouriSankarBhunia Department Of Vector Biology & Control, Rajendra Memorial Research Institute Of Medical Sciences (ICMR), Agamkuan, Patna – 800 007, Bihar, India India Geospatial Forum, 8th February, 2011

  2. 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.

  3. 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

  4. 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

  5. Objectives • Examining disease distribution and its relation with the environmental factors and vector distribution in a Kala-azar endemic region in Bihar, India

  6. Study Area BIHAR

  7. Data Used • Ground Survey Data – i. Disease Incidence Report 2005-2010 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)

  8. Methodology Field Survey Topographical Sheet Satellite data Pre-processing Climatic Data Epidemiological Data Entomological Data Integration in GIS Platform SAVI Statistical Analysis LST Estimation of Association Geo-statistical Analysis Spectral Analysis WI LULC Model Development for sandfly Prediction

  9. RESULTS Temporal distribution of cases and deaths of the study area

  10. Calculation of mean centre and directional distribution of disease

  11. Monthly distribution of sandfly density in the study site

  12. Sandfly characteristics of the study area

  13. Sandfly prediction based on inside room climate data R2 = 0.80; p-value <0.0000

  14. Relationship between maximum, minimum and mean SAVI value with sandfly density

  15. Wetness Index (WI) map of the Muzaffarpur district

  16. Land Surface Temperature (LST) of the study area, derived from Landsat- 5 TM

  17. Prediction of sandfly based environmental variables derived from remote sensing technology R2 = 0.85; p-value<0.001

  18. Land use/land cover (LULC) map of the study area

  19. Association between land use/land cover classes with the presence/absence of sandfly

  20. 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

  21. 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).

  22. Thank You

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