A Moran’s I Autocorrelation and Hot Spot Analysis for Identifying and Predicting Diarrheal Disease Cases around Sixty-Seven Community Wells in West Pokot County, Kenya
Ryan C Graydon*, Samuel Alao and Benjamin G Jacob
Department of Global Health, College of Public Health, University of South Florida, Tampa, USA
- *Corresponding Author:
- Ryan C Graydon
Department of Global Health, College of Public Health
University of South Florida, Tampa, USA
E-mail: [email protected]
Received Date: November 04, 2016; Accepted Date: November 25, 2016; Published Date: November 28, 2016
Citation: Graydon RC, Alao S, Jacob BG (2016) A Moran’s I Autocorrelation and Hot Spot Analysis for Identifying and Predicting Diarrheal Disease Cases around Sixty-Seven Community Wells in West Pokot County, Kenya. J Remote Sensing & GIS 5: 182. doi: 10.4182/2469-4134.1000182
Copyright: © 2016 Graydon RC, 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.
Water, sanitation, and hygiene (WASH) infrastructure is crucial to the health of every community. Globally, rural communities disproportionately lack improved drinking water and sanitation facilities compared to urban communities. West Pokot County, Kenya has a population of 512,690 people of which 91.7% live in rural areas. The Pokot people, the main people group residing in West Pokot County, depend on communal wells, rivers, and other surface water sources presenting the opportunity to consume pathogens and induce diarrheal diseases. Harvester’s International works with Pokot leaders to install community wells to provide an improved drinking water alternative to surface water in order to break the diarrheal disease transmission cycle. Community health epidemiological and spatial data from 67 communities in West Pokot County were assessed using autocorrelation and hot spot analysis in GIS software to identify geographical locations of reported cases of diarrheal diseases and to predict diarrheal disease cases across the entire county. The hot spot analysis revealed five hot spots and one cold spot and predicted additional hot spots in the southwest region of the county. This map is useful to target the specific locations for public health interventions to control and eliminate diarrheal diseases in West Pokot County. Future studies should include more spatial data points to improve the validity and reliability of the prediction map.