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

S. M. Niaz Arifin, Rumana Reaz Arifin, Dilkushi De Alwis Pitts, M. Sohel Rahman, Sara Nowreen, Gregory R. Madey and Frank H. Collins
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S. M. Niaz Arifin: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Rumana Reaz Arifin: Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
Dilkushi De Alwis Pitts: Center for Research Computing, University of Notre Dame, Notre Dame, IN 46556, USA
M. Sohel Rahman: Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1205, Bangladesh
Sara Nowreen: Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering andTechnology (BUET), Dhaka 1000, Bangladesh
Gregory R. Madey: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Frank H. Collins: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

Land, 2015, vol. 4, issue 2, 1-35

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.

Keywords: landscape epidemiology; agent-based models; simulation; modeling; spatial analysis; hot spot analysis; Kriging (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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