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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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 |
work_keys_str_mv |
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