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Petter Pilesjö

Petter Pilesjö

Professor

Petter Pilesjö

A spatially explicit agent-based modeling approach for the spread of Cutaneous Leishmaniasis disease in central Iran, Isfahan

Author

  • Mohammadreza Rajabi
  • Petter Pilesjö
  • Mohammad Reza Shirzadi
  • Reza Fadaei
  • Ali Mansourian

Summary, in English

Cutaneous Leishmaniasis (CL) is an endemic vector-borne disease in the Middle East and a worldwide public health problem. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and the environment. The heterogeneity of these interactions has hindered CL modeling for healthcare preventive measures in endemic areas. In this study, an agent-based model (ABM) is developed to simulate the dynamics of CL spread based on a Geographic Automata System (GAS). A Susceptible-Exposed-Infected-Recovered (SEIR) approach together with Bayesian modeling has been applied in the ABM to explore the spread of CL. The model is then adapted locally for Isfahan Province, an endemic area in central Iran. The results from the model indicate that desertification areas are the main origin of CL, and riverside population centers have the potential to host more sand fly exposures and should receive more preventive measures from healthcare authorities. The results also show that healthcare service accessibility prevented exposures from becoming infected and areas with new inhabitants experienced more infections from same amount of sand fly exposures.

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

Publishing year

2016-08-01

Language

English

Pages

330-346

Publication/Series

Environmental Modelling & Software

Volume

82

Document type

Journal article

Publisher

Elsevier

Topic

  • Geosciences, Multidisciplinary

Keywords

  • Agent-based model
  • Cutaneous Leishmaniasis
  • Disease modeling
  • Socio-ecological interactions
  • 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: 1364-8152