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Spatial analysis in epidemiology: Nascent science or a failure of GIS?

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  • Geoffrey M. Jacquez

    (BioMedware, Inc., 516 North State Street, Ann Arbor, MI 48104-1236, USA (e-mail: Jacquez@BioMedware.com))

Abstract

. This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology. Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information systems for epidemiology; the second by process-based disease models.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:jgeosy:v:2:y:2000:i:1:d:10.1007_s101090050035
    DOI: 10.1007/s101090050035
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    Citations

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    Cited by:

    1. Melissa Silva & Iuria Betco & César Capinha & Rita Roquette & Cláudia M. Viana & Jorge Rocha, 2022. "Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    2. Saturnino Luz & Masood Masoodian, 2022. "Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    3. Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    4. Renaud Marti & Zhichao Li & Thibault Catry & Emmanuel Roux & Morgan Mangeas & Pascal Handschumacher & Jean Gaudart & Annelise Tran & Laurent Demagistri & Jean-François Faure & José Joaquín Carvajal & , 2020. "A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires," Post-Print hal-02682042, HAL.
    5. Benjamin Adams, 2015. "Finding similar places using the observation-to-generalization place model," Journal of Geographical Systems, Springer, vol. 17(2), pages 137-156, April.
    6. Tao Hu & Qingyun Du & Fu Ren & Shi Liang & Denan Lin & Jiajia Li & Yan Chen, 2014. "Spatial Analysis of the Home Addresses of Hospital Patients with Hepatitis B Infection or Hepatoma in Shenzhen, China from 2010 to 2012," IJERPH, MDPI, vol. 11(3), pages 1-13, March.
    7. 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.
    8. Kandala, Ngianga-Bakwin & Magadi, Monica Akinyi & Madise, Nyovani Janet, 2006. "An investigation of district spatial variations of childhood diarrhoea and fever morbidity in Malawi," Social Science & Medicine, Elsevier, vol. 62(5), pages 1138-1152, March.
    9. I. Gede Nyoman M. Jaya & Henk Folmer, 2021. "Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 849-881, September.

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