-B8, 2012 
the relationship 
r density. The 
in. NDVI) were 
‚ as analyzed by 
e that measured 
and R? value 
1 has predicted 
ji villages have 
9)-environmental 
VEMM 
Iuno 
iuDeg 
uor 
epeues 
OMUEIN 
uedeyxyeuy 
| MHD 
   
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 
Reporting, Charting/statistics, Image Geotagging and 
Geoprocessing (Fig. 8). 
  
  
  
  
  
  
  
  
  
1 and predicted 
  
d (b) predicted 
’atepur Block, 
odel and fuzzy 
data of Patepur 
. prevalence of 
tlands and good 
ed using Open 
is successfully 
h has been used 
ıt of interest (in 
nd other climate 
determine the 
density. Thus 
wiate regression 
) use interface, 
lon —spatial data 
ition / Editing, 
Fig. 8 Main Viewer of Software package 
1.5 Conclusion 
The multivariate regression analysis between MHD and the 
predictor variables was found to hold true for the given 
epidemic area. Thus this statistical relation can be used in these 
regions for disease forewarning. This model has been applied 
successfully through 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. 
1.6 References 
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leishmaniasis in Bangalesh prospects for improved control; 
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2. Bhattacharya SK, Sur D, Sinha PK, Karbwang J, 2006. 
Elimination of leishmaniasis (Kala-azar) from the Indian 
subcontinent is technically feasible and operationally 
achievable, Indian J Med Res; 123:195-6 
3. Bora D, 1999. Epidemiology of visceral leishmaniasis in 
India, Natl Med J India; 12:62-8 
4. WHO Technical Report Series, No. 739, Control of the 
Leshmaniasis, 1990. Geneva, World Health Organisation. 
5. Jeyaram A, 2008, Water resource assessment and 
management using remote sensing and GIS, Umagani 
Watershed; PhD Thesis, Nagpur University. 
6. Kasi Mizanur Rahman, Shamim Islam, Muhammad Waliur 
Rahman, Eben Kenah, Chowdhury Mohammad Galive, M.M. 
Zahid, James Maguire, Mohmurur Rahman, Rashidul Haque, 
Stehen P. Luby and Caryn Bern, 2010. Increasing Incidence of 
Post-Kala-azar Dermal Leishmaniasis in a Population-based 
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7. Luis Fernando Chaves, Mercedes Pascual, 2007. Comparing 
Models for Early Warning Systems of Neglected Tropical 
Diseases, PLOS Neglected Tropical Dieases, volume 1, issue 1, 
e33. 
8. Patz JA, Campbell-Lendrum D, Holloway T, Folcy JA, 2005. 
Impact of regional climate change on human health, Nature 
438:310-317 
9. R.Kumar, P. Kumar, R.K. Chowdhary, K.Pai, C.P. Misra, 
K.Kumar, H.P Pandey, V.P. Singh and S.Sunder, 1999. Kala- 
azar epidemic in Varanasi district, India, Bulletin of World 
Health Organisation; 77(5). 
10. Saikat Paul, S Kesari, A Jeyaram, K. Kishore, A Ranjan, A 
Palit and V Jayaraman, 2007, EO — based study on sandfly 
vector (Kala-Azar disease) in Endemic and Non-Endemic area 
in Bihar and Jharkhand, India; IAC — 07- B1.5.04. 
11. Sanyal R.K.1985. Leishmanisis in the Indian sub- 
continent; In Chark KP, Bray R.S, eds. Leshmanisis , 
Amsterdam, Elsevier Science publisher, pp:443-467 
12. Shreekant Kesari, Gourisankar Bunia, Viya Kumar, 
Algarsamy Jeyaram, Alok Ranjan, Pradeep Das, 2010. Study of 
house-level risk factors associated in the transmission of Indian 
Kala-azar, Parasite & Vectors 2010, 3:94