Peer-reviewed veterinary case report
Analysis of time-space clustering in veterinary epidemiology.
- Journal:
- Preventive veterinary medicine
- Year:
- 2000
- Authors:
- Ward, M P & Carpenter, T E
- Affiliation:
- Queensland Department of Primary Industries · Australia
Plain-English summary
This study looked at how to find patterns in the occurrence of blowflies on a sheep farm in Tasmania between December 1997 and May 1998. Researchers used different methods to see if the blowflies were appearing in clusters, meaning they showed up more often in certain areas or times. They found that only one method, called Knox's test, showed significant clustering of the blowflies within a short distance of about 3 kilometers and over a month. The study suggests that using multiple methods can help get a clearer picture of whether events like this are happening in clusters. Overall, the findings highlight the importance of choosing the right techniques for analyzing such data.
Abstract
Techniques useful for investigating time-space interaction in the clustering of events in veterinary epidemiology--the Mantel test, Barton's method, nearest-neighbour test and Knox's test--are described. The use of these techniques is demonstrated by the analysis of a data set (containing blowfly catches on a commercial sheep property located in Tasmania, Australia, during the period December 1997-May 1998) in which clustering was apparent. Significant (P<0.05) clustering of blowfly catches in the data set was only detected by Knox's test. The use of Knox's test provided an insight into the possible clustering of blowfly catches over short (< or = 3 km) spatial and temporal (< or = 1 month) distances. Results demonstrate that several techniques should be used when attempting to identify and describe whether events are clustered in time and space (to maximise the power of the analysis). Also, it is important to consider the spatial and temporal models implicit in techniques chosen when interpreting results of analysis.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/10718492/