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.
(Less)
- author
- Rajabi, Mohammadreza
LU
; Pilesjö, Petter
LU
; Shirzadi, Mohammad Reza
; Fadaei, Reza
and Mansourian, Ali
LU
- organization
- publishing date
- 2016-08-01
- 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
-
- scopus:84969779017
- wos:000378954000024
- 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
- 2025-03-06 02:15:25
@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}},
}