This document discusses using remote sensing and GIS technologies to monitor vector-borne diseases. Specifically, it aims to determine how environmental factors like temperature, rainfall, humidity, forest cover, and water bodies affect disease occurrence. The methodology includes using regression analysis and geostatistical approaches to produce risk maps for predicting and controlling diseases. Remote sensing images and meteorological data are analyzed in GIS to map high, medium, and low risk areas. Radar images offer advantages for this type of analysis as they can image landscapes day or night, through clouds and haze. The results demonstrate how these tools can effectively model disease transmission and promote public health planning.

1. USE OF REMOTE SENSING AND GIS FOR
MONITORING VECTOR-BORNE DISEASES
M.Janaki
M.Tech (Geoinformatics)
I year
Reg.No: 3318201

2. INTRODUCTION
 Vector-Borne diseases like Malaria, Dengue,
Chikungunya, Japanese Encephalitis are serious
problems for the developing worlds like Asia, Africa
and South America.
 How modern technology like remote sensing GIS is
used in identification of environmental factors
which affect the spread of vector-borne diseases.

3. OBJECTIVE
• To do epidemiological and ecological study to
determine the effect of temperature, rainfall, humidity,
forest cover and water bodies on the occurrence of
vector-borne diseases.
 To produce to risk map to predict and control the
occurrence of diseases.
 To use the latest technological advances like remote
sensing and GIS for risk assessment and diseases
prevention and to promote overall healthcare.

4. METHODOLOGY
 Multiple Linear Regression analysis using backward
elimination method was done to determine the
predictor variables affecting the presence and the
incidence of malaria.
 For disease mapping or prediction geostatistical or
variogram approaches are used.
 They have suggested usage of ordinary kriging to
interpolate disease prevalence as recorded values at
known locations.
 This paper discusses RADARSAT – 1 image for malarial
risk mapping and advantages of using RADARSAT-2 in
future.

5. DATABASE
 Malarial case data were collected from the Directorate of Health
through various PHCs.
 Malarial positive cases were collected from 70 PHCs randomly
distributed on rural areas and for 4 urban areas (Salem, Mettur,
Attur and Edappadi).
 Villages with PHCs recorded with malarial cases were imported
into Mapinfo 6.0 to prepare thematic maps.
 Monthly meteorological data such as temperature, humidity and
rainfall of the study are collected from Department of
Meteorology, GOI.
 Forest, vegetation cover and water bodies corresponding to the
study are were extracted from IRS 1C LISS III remotely-sensed
images.
 Satellite data from RADARSAT-1 for coastal Kenya.

6. RESULTS
 In the first journal, the results show how temperature,
humidity, rainfall, vegetation cover and water bodies affect
the occurrence of malaria in the study area. They have
produced a prediction model which provides a detailed
mapping of high, medium and low incidence areas in Salem.
This map could be used for prediction and control of
malarial occurrences.
 In the third article, the results show the potential advantage
of radar remote sensing for classifying land covers
specifically as environmental variables relate to malaria
vector-breeding grounds. Radars are better suited because
of its capacity to penetrate clouds and haze and to image
both during day and night.

9. CONCLUSION
 From the three articles which were referred to we can understand
the effect of environmental factors like rainfall, humidity,
vegetation cover and water bodies on the occurrence of vector-
borne diseases especially malaria.
 We can also understand how remote sensing and GIS could be
used for risk mapping and prediction and promotion of health.
 We can also infer the advantage of using radar images as they
can be used under all weathers and not affected by presence of
clouds or haze.
 Finally, all this helps us understand how we can produce a risk
map of a particular study are for vector-borne diseases and how
this risk map could be used for prediction and prevention and
overall planning of disease control.

10. REFERENCES
 Use of Remote Sensing and GIS for monitoring Environmental factors associated
with Vector-Borne Disease [Indian Geographic Journal, June and December 2006,
Volume 81(1&2) ISSN 0019 – 4824 page number 47 – 60] (By M. Prasahanthi Devi
and Balasubramanian, Department of Environmental Sciences, Bharathiar
University, Coimbatore, India and B.Manickam, ISRO Head Quarters, Bangalore,
India).
 Spatial Temporal Analysis of Vector- Borne Diseases in Mysore District
[International Journal of Life Sciences, Vol.2 N.12013 pages 43 – 52 ISSN: 2227 –
193X] (By Minutha. V and Subhash.S.Sanasiddanannavar, Department of
Geography University of Mysore)
 Monitoring environmental indicators of Vector-Borne disease from space: a new
opportunity for RADARSAT – 2 [Canadian Journal of Remote Sensing, Vol. 30, No.3
pages: 560 – 565, 2004] (By S.Kaya, J. Sokol and T.J. Pultz).
 International conference on Globalisation and Sustainable Development
perspective of Digital Revolution and Environmental Management. [Aug 2002,
Vol.1 page : 23 – 25]

11. THANK YOU