alexa Mapping Minimum and Maximum Standard Deviation 3-Dimensionsional Slope Coefficients for Geo-spectrotemporally Iteratively Quantitatively Interpolating an End-member Proxy Signature of Cyanobacteria (Bluegreen Algae) for Eco-cartographically Delineating Cholera Risk in a Riverine Tributary Ecosystem in Ecuador
ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Mapping Minimum and Maximum Standard Deviation 3-Dimensionsional Slope Coefficients for Geo-spectrotemporally Iteratively Quantitatively Interpolating an End-member Proxy Signature of Cyanobacteria (Bluegreen Algae) for Eco-cartographically Delineating Cholera Risk in a Riverine Tributary Ecosystem in Ecuador

Sarita Panchang*, Samuel Alao, Jacob GB and Ricardo Izurieta

Department of Global Health, College of Public Health, University of South Florida, Tampa, FL 33613, USA

*Corresponding Author:
Sarita Panchang
Department of Global Health, College of Public Health
University of South Florida, Tampa, FL 33613, USA
Tel: +8139742011
E-mail: [email protected]

Received Date: November 04, 2016; Accepted Date: November 17, 2016; Published Date: November 21, 2016

Citation: Panchang S, Alao S, Jacob GB, Izurieta R (2016) Mapping Minimum and Maximum Standard Deviation 3-Dimensionsional Slope Coefficients for Geo-spectrotemporally Iteratively Quantitatively Interpolating an End-member Proxy Signature of Cyanobacteria (Blue-green Algae) for Eco-cartographically Delineating Cholera Risk in a Riverine Tributary Ecosystem in Ecuador. J Remote Sensing & GIS 5:180. doi: 10.4180/2469-4134.1000180

Copyright: © 2016 Panchang S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Cholera, caused by the bacteria Vibrio cholerae, hit Latin America after nearly a century in 1991. Ecuador was one of the most highly impacted regions by morbidity. While a number of infrastructural and socially relevant factors have been cited in cholera transmission, a growing body of work also points to the relevance of ecological factors, namely the presence and cycle of copepods and blue-green algae which may harbor the bacteria at certain times. The goal of this study was to demonstrate the utility of an interpolation technique which could be used alongside cholera case rate data to determine a predictive environmental signature for cholera risk areas in the future.

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