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Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology

Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology

Sharmila Banu K., B.K. Tripathy
Copyright: © 2016 |Volume: 3 |Issue: 1 |Pages: 10
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781466694002|DOI: 10.4018/IJRSDA.2016010107
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MLA

K., Sharmila Banu, and B.K. Tripathy. "Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology." IJRSDA vol.3, no.1 2016: pp.114-123. http://doi.org/10.4018/IJRSDA.2016010107

APA

K., S. B. & Tripathy, B. (2016). Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(1), 114-123. http://doi.org/10.4018/IJRSDA.2016010107

Chicago

K., Sharmila Banu, and B.K. Tripathy. "Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology," International Journal of Rough Sets and Data Analysis (IJRSDA) 3, no.1: 114-123. http://doi.org/10.4018/IJRSDA.2016010107

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Abstract

Epidemiological studies are carried out to understand the pattern and transmission of disease instances. Some prominent dimensions considered for analysis are cohort studies, ecological studies, transmission modeling and prediction. ‘Descriptive Epidemiology' is defined with respect to ‘people, time and place'. Place (geography) plays a key role in the pattern of disease outcomes in both epidemic outbreaks and chronic cases. A lot of research has documented the significance of spatial features in Epidemiology and have produced health/disease maps of a particular geography. This work proposes to identify similarity between regions in such maps using Rough set based measures. Thus spatial auto-correlation of disease instances in a geographic region can be analysed further to prepare mitigation strategies.

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