A spatially explicit agent-based modeling approach for the spread of Cutaneous Leishmaniasis disease in central Iran, Isfahan. (August 2016)

• Record Type: Journal Article
• Authors: Rajabi, Mohammadreza; Pilesjö, Petter; Shirzadi, Mohammad Reza; Fadaei, Reza; Mansourian, Ali
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 330
• Page End: 346
• Publication Date: 2016-08
• Subjects: Cutaneous Leishmaniasis -- Agent-based model -- Disease modeling -- Socio-ecological interactions; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.006
• Languages: English
• ISSNs: 1364-8152

Abstract

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 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. Highlights: CL spread is modeled using an ABM in the most endemic area of Iran. A SEIR approach together with Bayesian modeling is applied in the ABM. Four common challenges of ABMs are addressed in this study. A GSUA is applied to investigate the uncertainty of the model.