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A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran

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Geospatial Information Science Division, Faculty of Geodesy and Geomatics Engineering, and Center of Excellence in Geo-Information Technology, K.N. Toosi University of Technology, Tehran 1996715433, Iran
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Department of Surveying Engineering, College of Earth Sciences Engineering, Arak University of Technology, Arak 3818146763, Iran
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Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, 3584 CB Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 549; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090549
Received: 17 August 2020 / Revised: 9 September 2020 / Accepted: 13 September 2020 / Published: 15 September 2020
Malaria threatens the lives of many people throughout the world. To counteract its spread, knowledge of the prevalence of malaria and the effectiveness of intervention strategies is of great importance. The aim of this study was to assess (1) the spread of malaria by means of a spatial agent-based model (ABM) and (2) the effectiveness of several interventions in controlling the spread of malaria. We focused on Sarbaz county in Iran, a malaria-endemic area where the prevalence rate is high. Our ABM, which was carried out in two steps, considers humans and mosquitoes along with their attributes and behaviors as agents, while the environment is made up of diverse environmental factors, namely air temperature, relative humidity, vegetation, altitude, distance from rivers and reservoirs, and population density, the first three of which change over time. As control interventions, we included long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). The simulation results showed that applying LLINs and IRS in combination, rather than separately, was most efficient in reducing the number of infected humans. In addition, LLINs and IRS with moderate or high and high coverage rates, respectively, had significant effects on reducing the number of infected humans when applied separately. Our results can assist health policymakers in selecting appropriate intervention strategies in Iran to reduce malaria transmission. View Full-Text
Keywords: agent-based model; malaria; health; intervention; simulation; geospatial information science agent-based model; malaria; health; intervention; simulation; geospatial information science
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MDPI and ACS Style

Gharakhanlou, N.M.; Hooshangi, N.; Helbich, M. A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran. ISPRS Int. J. Geo-Inf. 2020, 9, 549. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090549

AMA Style

Gharakhanlou NM, Hooshangi N, Helbich M. A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran. ISPRS International Journal of Geo-Information. 2020; 9(9):549. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090549

Chicago/Turabian Style

Gharakhanlou, Navid M., Navid Hooshangi, and Marco Helbich. 2020. "A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran" ISPRS International Journal of Geo-Information 9, no. 9: 549. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090549

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