The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Ali Mansourian

Ali Mansourian

Professor

Ali Mansourian

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks : a case study in north-western Iran

Author

  • Mohammadreza Rajabi
  • Ali Mansourian
  • Petter Pilesjö
  • Ahad Bazmani

Summary, in English

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.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • Centre for Geographical Information Systems (GIS Centre)
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World
  • eSSENCE: The e-Science Collaboration

Publishing year

2014

Language

English

Pages

179-191

Publication/Series

Geospatial health

Volume

9

Issue

1

Document type

Journal article

Publisher

University of Naples Federico II

Topic

  • Human Geography
  • Physical Geography

Keywords

  • visceral leishmaniasis
  • environment
  • geographical information systems
  • neural networks
  • Artificial Intelligence (AI)
  • Geospatial Artificial Intelligence (GeoAI)

Status

Published

Project

  • Geospatial modeling and simulation techniques to study prevalence and spread of diseases

ISBN/ISSN/Other

  • ISSN: 1970-7096