Livestock Disease Data Management for E-Surveillance and Disease Mapping Using Cluster Analysis

Abstract

This study investigates how Electronic Livestock Health Recording Systems (ELHRs)
 facilitates the detection of disease burden and make cluster analysis by applying data
 analytics tools and techniques. A sample size of 18333 livestock disease cases reported from
 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for
 data collection. The results showed that ELHRs are important as livestock disease data
 preservers, saving costs, and facilitating the extraction of up-to-date and complete
 information. Euclidean and Manhattan distance performed well at 98%, while cosine distance
 measurement metrics performed poorly. Finally, with the application of the selected
 clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect
 an optimal number of disease clusters and meet the objectives of the study.