Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System

A landscape epidemiology modeling framework is presented which integrates the simulation outputs from an established spatial agent-based model (ABM) of malaria with a geographic information system (GIS). For a study area in Kenya, five landscape scenarios are constructed with varying coverage levels...

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Main Authors: S. M. Niaz Arifin, Rumana Reaz Arifin, Dilkushi de Alwis Pitts, M. Sohel Rahman, Sara Nowreen, Gregory R. Madey, Frank H. Collins
Format: Article
Language:English
Published: MDPI AG 2015-05-01
Series:Land
Subjects:
Online Access:http://www.mdpi.com/2073-445X/4/2/378
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spelling doaj-02098e78c92140e789cc80be51670a152020-11-24T22:45:30ZengMDPI AGLand2073-445X2015-05-014237841210.3390/land4020378land4020378Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information SystemS. M. Niaz Arifin0Rumana Reaz Arifin1Dilkushi de Alwis Pitts2M. Sohel Rahman3Sara Nowreen4Gregory R. Madey5Frank H. Collins6Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USADepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USACenter for Research Computing, University of Notre Dame, Notre Dame, IN 46556, USADepartment of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1205, BangladeshInstitute of Water and Flood Management (IWFM), Bangladesh University of Engineering andTechnology (BUET), Dhaka 1000, BangladeshDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USADepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USAA landscape epidemiology modeling framework is presented which integrates the simulation outputs from an established spatial agent-based model (ABM) of malaria with a geographic information system (GIS). For a study area in Kenya, five landscape scenarios are constructed with varying coverage levels of two mosquito-control interventions. For each scenario, maps are presented to show the average distributions of three output indices obtained from the results of 750 simulation runs. Hot spot analysis is performed to detect statistically significant hot spots and cold spots. Additional spatial analysis is conducted using ordinary kriging with circular semivariograms for all scenarios. The integration of epidemiological simulation-based results with spatial analyses techniques within a single modeling framework can be a valuable tool for conducting a variety of disease control activities such as exploring new biological insights, monitoring epidemiological landscape changes, and guiding resource allocation for further investigation.http://www.mdpi.com/2073-445X/4/2/378landscape epidemiologyagent-based modelssimulationmodelingspatial analysishot spot analysisKriging
collection DOAJ
language English
format Article
sources DOAJ
author S. M. Niaz Arifin
Rumana Reaz Arifin
Dilkushi de Alwis Pitts
M. Sohel Rahman
Sara Nowreen
Gregory R. Madey
Frank H. Collins
spellingShingle S. M. Niaz Arifin
Rumana Reaz Arifin
Dilkushi de Alwis Pitts
M. Sohel Rahman
Sara Nowreen
Gregory R. Madey
Frank H. Collins
Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
Land
landscape epidemiology
agent-based models
simulation
modeling
spatial analysis
hot spot analysis
Kriging
author_facet S. M. Niaz Arifin
Rumana Reaz Arifin
Dilkushi de Alwis Pitts
M. Sohel Rahman
Sara Nowreen
Gregory R. Madey
Frank H. Collins
author_sort S. M. Niaz Arifin
title Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
title_short Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
title_full Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
title_fullStr Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
title_full_unstemmed Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System
title_sort landscape epidemiology modeling using an agent-based model and a geographic information system
publisher MDPI AG
series Land
issn 2073-445X
publishDate 2015-05-01
description A landscape epidemiology modeling framework is presented which integrates the simulation outputs from an established spatial agent-based model (ABM) of malaria with a geographic information system (GIS). For a study area in Kenya, five landscape scenarios are constructed with varying coverage levels of two mosquito-control interventions. For each scenario, maps are presented to show the average distributions of three output indices obtained from the results of 750 simulation runs. Hot spot analysis is performed to detect statistically significant hot spots and cold spots. Additional spatial analysis is conducted using ordinary kriging with circular semivariograms for all scenarios. The integration of epidemiological simulation-based results with spatial analyses techniques within a single modeling framework can be a valuable tool for conducting a variety of disease control activities such as exploring new biological insights, monitoring epidemiological landscape changes, and guiding resource allocation for further investigation.
topic landscape epidemiology
agent-based models
simulation
modeling
spatial analysis
hot spot analysis
Kriging
url http://www.mdpi.com/2073-445X/4/2/378
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