Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks : a case study in north-western Iran
Rajabi, Mohammadreza LU ; Mansourian, Ali LU
; Pilesjö, Petter
LU
and Bazmani, Ahad
(2014)
In Geospatial health
9(1).
p.179-191
- Abstract
- Visceral leishmaniasis (VL) is a potentially fatal vector-borne zoonotic disease, which has become an increasing public health problem in the north-western part of Iran. This work presents an environmental health modelling approach to map the potential of VL outbreaks in this part of the country. Radial basis functional link networks is used as a data-driven method for predictive mapping of VL in the study area. The high susceptibility areas for VL outbreaks account for 36.3% of the study area and occur mainly in the north (which may affect the neighbouring countries) and South (which is a warning for other provinces in Iran). These parts of the study area have many nomadic, riverside villages. The overall accuracy of the resultant map was... (More)
- Visceral leishmaniasis (VL) is a potentially fatal vector-borne zoonotic disease, which has become an increasing public health problem in the north-western part of Iran. This work presents an environmental health modelling approach to map the potential of VL outbreaks in this part of the country. Radial basis functional link networks is used as a data-driven method for predictive mapping of VL in the study area. The high susceptibility areas for VL outbreaks account for 36.3% of the study area and occur mainly in the north (which may affect the neighbouring countries) and South (which is a warning for other provinces in Iran). These parts of the study area have many nomadic, riverside villages. The overall accuracy of the resultant map was 92% in endemic villages. Such susceptibility maps can be used as reconnaissance guides for planning of effective control strategies and identification of possible new VL endemic areas. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4944763
- author
- Rajabi, Mohammadreza
LU
; Mansourian, Ali
LU
; Pilesjö, Petter
LU
and Bazmani, Ahad
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- visceral leishmaniasis, environment, geographical information systems, neural networks, Artificial Intelligence (AI), Geospatial Artificial Intelligence (GeoAI)
- in
- Geospatial health
- volume
- 9
- issue
- 1
- pages
- 179 - 191
- publisher
- University of Naples Federico II
- external identifiers
-
- wos:000346512600015
- pmid:25545935
- scopus:84911888514
- pmid:25545935
- ISSN
- 1970-7096
- DOI
- 10.4081/gh.2014.15
- project
- Geospatial modeling and simulation techniques to study prevalence and spread of diseases
- language
- English
- LU publication?
- yes
- id
- a22ea719-9a4f-4967-9267-2d2f01def4c4 (old id 4944763)
- date added to LUP
- 2016-04-01 11:00:21
- date last changed
- 2025-01-14 04:03:55
@article{a22ea719-9a4f-4967-9267-2d2f01def4c4,
abstract = {{Visceral leishmaniasis (VL) is a potentially fatal vector-borne zoonotic disease, which has become an increasing public health problem in the north-western part of Iran. This work presents an environmental health modelling approach to map the potential of VL outbreaks in this part of the country. Radial basis functional link networks is used as a data-driven method for predictive mapping of VL in the study area. The high susceptibility areas for VL outbreaks account for 36.3% of the study area and occur mainly in the north (which may affect the neighbouring countries) and South (which is a warning for other provinces in Iran). These parts of the study area have many nomadic, riverside villages. The overall accuracy of the resultant map was 92% in endemic villages. Such susceptibility maps can be used as reconnaissance guides for planning of effective control strategies and identification of possible new VL endemic areas.}},
author = {{Rajabi, Mohammadreza and Mansourian, Ali and Pilesjö, Petter and Bazmani, Ahad}},
issn = {{1970-7096}},
keywords = {{visceral leishmaniasis; environment; geographical information systems; neural networks; Artificial Intelligence (AI); Geospatial Artificial Intelligence (GeoAI)}},
language = {{eng}},
number = {{1}},
pages = {{179--191}},
publisher = {{University of Naples Federico II}},
series = {{Geospatial health}},
title = {{Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks : a case study in north-western Iran}},
url = {{http://dx.doi.org/10.4081/gh.2014.15}},
doi = {{10.4081/gh.2014.15}},
volume = {{9}},
year = {{2014}},
}