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Land 2015, 4(2), 378-412; doi:10.3390/land4020378

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

1
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
2
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
3
Center for Research Computing, University of Notre Dame, Notre Dame, IN 46556, USA
4
Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1205, Bangladesh
5
Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering andTechnology (BUET), Dhaka 1000, Bangladesh
6
Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
*
Author to whom correspondence should be addressed.
Academic Editors: James Millington and John Wainwright
Received: 5 January 2015 / Revised: 17 April 2015 / Accepted: 4 May 2015 / Published: 13 May 2015
(This article belongs to the Special Issue Agent-Based Modelling and Landscape Change)
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Abstract

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. View Full-Text
Keywords: landscape epidemiology; agent-based models; simulation; modeling; spatial analysis; hot spot analysis; Kriging landscape epidemiology; agent-based models; simulation; modeling; spatial analysis; hot spot analysis; Kriging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Arifin, S.M.N.; Arifin, R.R.; Pitts, D.A.; Rahman, M.S.; Nowreen, S.; Madey, G.R.; Collins, F.H. Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System. Land 2015, 4, 378-412.

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