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Exploring Incidence-Prevalence Patterns in Spatial Epidemiology via Neighborhood Rough Sets

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  • Sharmila Banu K.

    (VIT University, Vellore, India)

  • B.K. Tripathy

    (School of Computing Science and Engineering, VIT University, Vellore, India)

Abstract

Epidemiological studies are largely purposed to provide outcomes that may be used for interventions and development programs. In recent years, geo-referencing of epidemiological data has become one of the vital features. Often, epidemiological data collected for regions under study will show areas that are affected with certain diseases in the form of incidence or prevalence information. As well, such information may be spatially mapped and used for further analysis on pattern comparisons. A key objective of such analytic works would be to come up with effective interventions, including planning for remedial measures. In this paper, the authors propose the use of Neighborhood Rough Set Theory (NRST) to be applied innovatively within a mapped area featured with incident-prevalent cases of a disease and computing the similarity patterns among various affected areas in a region. The similarity statistics and/or indices thus computed may aid in planning remedial measures in a geographic region whose sub-regions are marked with various incidence-prevalence information.

Suggested Citation

  • Sharmila Banu K. & B.K. Tripathy, 2017. "Exploring Incidence-Prevalence Patterns in Spatial Epidemiology via Neighborhood Rough Sets," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(1), pages 30-43, January.
    • Handle: RePEc:igg:jhisi0:v:12:y:2017:i:1:p:30-43
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