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

Rajabi, Mohammadreza LU ; Pilesjö, Petter LU ; Shirzadi, Mohammad Reza ; Fadaei, Reza and Mansourian, Ali LU (2016) In Environmental Modelling & Software 82. p.330-346
Abstract

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... (More)

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.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Agent-based model, Cutaneous Leishmaniasis, Disease modeling, Socio-ecological interactions, Artificial Intelligence (AI), Geospatial Artificial Intelligence (GeoAI)
in
Environmental Modelling & Software
volume
82
pages
17 pages
publisher
Elsevier
external identifiers
  • wos:000378954000024
  • scopus:84969779017
ISSN
1364-8152
DOI
10.1016/j.envsoft.2016.04.006
project
Geospatial modeling and simulation techniques to study prevalence and spread of diseases
language
English
LU publication?
yes
id
b83706df-83f0-4b30-8795-268ff446e8d8
date added to LUP
2016-06-07 08:46:42
date last changed
2024-02-02 19:13:42
@article{b83706df-83f0-4b30-8795-268ff446e8d8,
  abstract     = {{<p>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.</p>}},
  author       = {{Rajabi, Mohammadreza and Pilesjö, Petter and Shirzadi, Mohammad Reza and Fadaei, Reza and Mansourian, Ali}},
  issn         = {{1364-8152}},
  keywords     = {{Agent-based model; Cutaneous Leishmaniasis; Disease modeling; Socio-ecological interactions; Artificial Intelligence (AI); Geospatial Artificial Intelligence (GeoAI)}},
  language     = {{eng}},
  month        = {{08}},
  pages        = {{330--346}},
  publisher    = {{Elsevier}},
  series       = {{Environmental Modelling & Software}},
  title        = {{A spatially explicit agent-based modeling approach for the spread of Cutaneous Leishmaniasis disease in central Iran, Isfahan}},
  url          = {{http://dx.doi.org/10.1016/j.envsoft.2016.04.006}},
  doi          = {{10.1016/j.envsoft.2016.04.006}},
  volume       = {{82}},
  year         = {{2016}},
}