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

Author

Listed:
  • 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
    Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA)

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.

Suggested Citation

  • S. M. Niaz Arifin & Rumana Reaz Arifin & Dilkushi De Alwis Pitts & M. Sohel Rahman & Sara Nowreen & Gregory R. Madey & Frank H. Collins, 2015. "Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System," Land, MDPI, vol. 4(2), pages 1-35, May.
    • Handle: RePEc:gam:jlands:v:4:y:2015:i:2:p:378-412:d:49561
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    References listed on IDEAS

    as
    1. Peter W Gething & Anand P Patil & Simon I Hay, 2010. "Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-12, April.
    2. Peter Diggle & Rana Moyeed & Barry Rowlingson & Madeleine Thomson, 2002. "Childhood malaria in the Gambia: a case‐study in model‐based geostatistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 493-506, October.
    3. Geoffrey M. Jacquez, 2000. "Spatial analysis in epidemiology: Nascent science or a failure of GIS?," Journal of Geographical Systems, Springer, vol. 2(1), pages 91-97, March.
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    Cited by:

    1. James D. A. Millington & John Wainwright, 2016. "Comparative Approaches for Innovation in Agent-Based Modelling of Landscape Change," Land, MDPI, vol. 5(2), pages 1-4, May.

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