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Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases

Author

Listed:
  • José Miguel Barrios

    (Biosystems Department M3-BIORES, KU Leuven, Willem de Croylaan 34 B3001, Heverlee, Belgium)

  • Willem W. Verstraeten

    (Climate Observations, Royal Netherlands Meteorological Institute, PO Box 201 NL-3730 AE, De Bilt, The Netherlands
    Applied Physics, Eindhoven University of Technology, PO Box 513 5600 MB, Eindhoven, The Netherlands)

  • Piet Maes

    (Laboratory of Clinical Virology, National Reference Laboratory for Hantaviruses, KU Leuven, Minderbroedersstraat 10 B3000, Leuven, Belgium)

  • Jean-Marie Aerts

    (Biosystems Department M3-BIORES, KU Leuven, Willem de Croylaan 34 B3001, Heverlee, Belgium)

  • Jamshid Farifteh

    (Biosystems Department M3-BIORES, KU Leuven, Willem de Croylaan 34 B3001, Heverlee, Belgium)

  • Pol Coppin

    (Biosystems Department M3-BIORES, KU Leuven, Willem de Croylaan 34 B3001, Heverlee, Belgium)

Abstract

The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases.

Suggested Citation

  • José Miguel Barrios & Willem W. Verstraeten & Piet Maes & Jean-Marie Aerts & Jamshid Farifteh & Pol Coppin, 2012. "Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases," IJERPH, MDPI, vol. 9(12), pages 1-19, November.
    • Handle: RePEc:gam:jijerp:v:9:y:2012:i:12:p:4346-4364:d:21867
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    References listed on IDEAS

    as
    1. Nijkamp, Peter, 1975. "Reflections on gravity and entropy models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 203-225, May.
    2. Xinhai Li & Huidong Tian & Dejian Lai & Zhibin Zhang, 2011. "Validation of the Gravity Model in Predicting the Global Spread of Influenza," IJERPH, MDPI, vol. 8(8), pages 1-10, July.
    3. Gatrell, A. C. & Bailey, T. C., 1996. "Interactive spatial data analysis in medical geography," Social Science & Medicine, Elsevier, vol. 42(6), pages 843-855, March.
    4. John Roy & Jean-Claude Thill, 2003. "Spatial interaction modelling," Economics of Governance, Springer, vol. 83(1), pages 339-361, October.
    5. John Roy & Jean-Claude Thill, 2003. "Spatial interaction modelling," Papers in Regional Science, Springer;Regional Science Association International, vol. 83(1), pages 339-361, October.
    6. Potapov, Alex & Muirhead, Jim R. & Lele, Subhash R. & Lewis, Mark A., 2011. "Stochastic gravity models for modeling lake invasions," Ecological Modelling, Elsevier, vol. 222(4), pages 964-972.
    7. Roger J. Marshall, 1991. "Mapping Disease and Mortality Rates Using Empirical Bayes Estimators," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(2), pages 283-294, June.
    8. Glass, G.E. & Schwartz, B.S. & Morgan III, J.M. & Johnson, D.T. & Noy, P.M. & Israel, E., 1995. "Environmental risk factors for Lyme disease identified with geographic information systems," American Journal of Public Health, American Public Health Association, vol. 85(7), pages 944-948.
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    Cited by:

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    4. Jia-Bao Liu & Xin-Bei Peng & Jing Zhao, 2023. "Analyzing the spatial association of household consumption carbon emission structure based on social network," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-34, March.

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