IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v9y2012i12p4346-4364d21867.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/9/12/4346/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/9/12/4346/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. John Roy & Jean-Claude Thill, 2003. "Spatial interaction modelling," Economics of Governance, Springer, vol. 83(1), pages 339-361, October.
    3. Nijkamp, Peter, 1975. "Reflections on gravity and entropy models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 203-225, May.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    2. Ribeiro, Fabiano L. & Li, Yunfei & Born, Stefan & Rybski, Diego, 2024. "Analytical solution for the long- and short-range every-pair-interactions system," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    3. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard Fry & Scott Orford & Sarah Rodgers & Jennifer Morgan & David Fone, 2020. "A best practice framework to measure spatial variation in alcohol availability," Environment and Planning B, , vol. 47(3), pages 381-399, March.
    2. Kyosang Hwang & Tooba Binte Asif & Taesik Lee, 2022. "Choice-driven location-allocation model for healthcare facility location problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 1040-1065, December.
    3. Arvis, Jean-François, 2013. "Integrating gravity: the role of scale invariance in gravity models of spatial interactions and trade," Policy Research Working Paper Series 6347, The World Bank.
    4. Chakraborty, A. & Beamonte, M.A. & Gelfand, A.E. & Alonso, M.P. & Gargallo, P. & Salvador, M., 2013. "Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 292-307.
    5. Martina Neuländtner & Thomas Scherngell, 2020. "Geographical or relational: What drives technology-specific R&D collaboration networks?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(3), pages 743-773, December.
    6. Supachai Nakapan & Nitin Kumar Tripathi & Taravudh Tipdecho & Marc Souris, 2012. "Spatial Diffusion of Influenza Outbreak-Related Climate Factors in Chiang Mai Province, Thailand," IJERPH, MDPI, vol. 9(11), pages 1-19, October.
    7. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    8. Laura A. Rodriguez-Villamizar & Diana Marín & Juan Gabriel Piñeros-Jiménez & Oscar Alberto Rojas-Sánchez & Jesus Serrano-Lomelin & Victor Herrera, 2023. "Intraurban Geographic and Socioeconomic Inequalities of Mortality in Four Cities in Colombia," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    9. Wang, Cheng & Wang, Gang & Guo, Ziru & Dai, Lingjun & Liu, Hongyu & Li, Yufeng & Chen, Hao & Zhao, Yongxiang & Zhang, Yanan & Cheng, Hai, 2020. "Effects of land-use change on the distribution of the wintering red-crowned crane (Grus japonensis) in the coastal area of northern Jiangsu Province, China," Land Use Policy, Elsevier, vol. 90(C).
    10. Phaisarn Jeefoo & Nitin Kumar Tripathi & Marc Souris, 2010. "Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand," IJERPH, MDPI, vol. 8(1), pages 1-24, December.
    11. Emílio Prado da Fonseca & Regiane Cristina do Amaral & Antonio Carlos Pereira & Carla Martins Rocha & Marc Tennant, 2018. "Geographical Variation in Oral and Oropharynx Cancer Mortality in Brazil: A Bayesian Approach," IJERPH, MDPI, vol. 15(12), pages 1-9, November.
    12. Caggiani, Leonardo & Ottomanelli, Michele & Dell’Orco, Mauro, 2014. "Handling uncertainty in Multi Regional Input-Output models by entropy maximization and fuzzy programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 159-172.
    13. repec:hum:wpaper:sfb649dp2015-048 is not listed on IDEAS
    14. Yifan Li & Juanle Wang & Mengxu Gao & Liqun Fang & Changhua Liu & Xin Lyu & Yongqing Bai & Qiang Zhao & Hairong Li & Hongjie Yu & Wuchun Cao & Liqiang Feng & Yanjun Wang & Bin Zhang, 2017. "Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China," IJERPH, MDPI, vol. 14(6), pages 1-18, May.
    15. Karen Witten & Daniel Exeter & Adrian Field, 2003. "The Quality of Urban Environments: Mapping Variation in Access to Community Resources," Urban Studies, Urban Studies Journal Limited, vol. 40(1), pages 161-177, January.
    16. Francis Markham & Bruce Doran & Martin Young, 2014. "The Spatial Extents of Casino Catchments in Australia," Growth and Change, Wiley Blackwell, vol. 45(1), pages 60-78, March.
    17. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312.
    18. Nijkamp, Peter & Poot, Jacques, 1987. "Dynamics of generalised spatial interaction models," Regional Science and Urban Economics, Elsevier, vol. 17(3), pages 367-390, August.
    19. Haozhe Zhang & Jinyi Li, 2024. "Mapping the urban and rural planning response paths to pandemics of infectious diseases," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    20. Rembrandt D. Scholz & Sebastian Klüsener, 2012. "Regional hot spots of exceptional longevity in Germany," MPIDR Working Papers WP-2012-028, Max Planck Institute for Demographic Research, Rostock, Germany.
    21. Russo, Francesco & Musolino, Giuseppe, 2013. "Estimating demand variables of maritime container transport: An aggregate procedure for the Mediterranean area," Research in Transportation Economics, Elsevier, vol. 42(1), pages 38-49.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:9:y:2012:i:12:p:4346-4364:d:21867. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.