An Informed Virtual Geographic Environment Enhanced with Qualitative and Quantitative Information for the Geosimulations of Zoonosis Propagation
Public health decision makers need to better understand the propagation of zoonoses. The currently available zoonosis simulations are based on compartment models which do not integrate the influence of geographic features on the species’ biological processes. In this context, we propose an approach that can generate an informed virtual geographic environment (IVGE) composed of a set of cells in which the evolution and interaction of the involved populations can be simulated plausibly. Since the number of these cells is huge, we propose a threshold-based merging algorithm that creates spatial subdivisions with the maximal size and suitability for a given biological phenomenon. Our approach also enhances each cell with qualitative and quantitative information such as the relative geographic orientation of the neighbors and the information about the distribution of individuals through trajectories. We used our IVGE to develop decision support tools that can simulate the spread of the West Nile Virus and Lyme disease.
KeywordsGeosimulation Zoonosis propagation Merging process Spatial distribution Virtual environment
Many thanks to GEOIDE, the Canadian network of centers of excellence in geomatics (CODIGEOSIM Project), INSPQ (Institut national de santé publique du Québec) and the Saint-Hyacinthe Division of the Public Health Agency of Canada (PHAC) for their support (finance, expertise and data).
- Bouden M, Moulin B (2012a) A interaction model used to geosimulate the zoonosis propagation. In: Symposium on theory of modeling and simulation (TMS’12), spring simulation multi-conference, the society for modeling and simulation international, 26–29 March, Orlando, USAGoogle Scholar
- Bouden M, Moulin B (2012b) Zoonosis-MAGS: a generic multi-level geosimulation tool for zoonosis propagation. In: Global geospatial conference, spatially enabling government, industry and citizens. Quebec City, Canada, 14–17 May 2012Google Scholar
- Bouden M, Moulin B, Gosselin P (2008) The geosimulation of West Nile virus propagation: a tool for risk management in public health. Int J Health Geogr 7(35):1–19Google Scholar
- Emrich S, Suslov S, Judex F (2007) Fully agent based modelling of epidemic spread using anylogic. In: Proceedings of EUROSIM, September 2007, Ljubljana, SloveniaGoogle Scholar
- Francis CM, Hussell DJT (1998) Changes in numbers of land birds counted in migration at long point bird observatory, 1961–1997. Bird Popul 5(6):37–66Google Scholar
- Gosselin P, Lebel G, Rivest S, Douville-Fradet M (2005) The integrated system for public health monitoring of West Nile virus (ISPHM-WNV): a real-time GIS for surveillance and decision-making. Int J Health Geogr 4(21):1–12Google Scholar
- Lisichenko R (2008) GIS using geomedia professional V6, 1st edn., Word PressGoogle Scholar
- Liu R, Shuai J, Zhu H, Wu J (2006) Modeling spatial spread of West Nile virus and impact of directional dispersal of birds. Math Biosci Eng 3:145–160Google Scholar
- White SH, Martin del Rey A, Rodriguez Sanchez G (2009) Using cellular automata to simulate epidemic diseases. Appl Math Sci 3(20):959–968Google Scholar