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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/1979

Title: Modeling diapause termination of Rhipicephalus appendiculatus using statistical tools to detect sudden behavioral changes and time dependencies
Authors: Speybroeck, N
LINDSEY, Patrick
Billiouw, M
Madder, M
Berkvens, D.L.
Issue Date: 2006
Publisher: SPRINGER
Abstract: This paper presents statistical methodology to analyze longitudinal binary responses for which a sudden change in the response occurs in time. Probability plots, transition matrices, and change-point models and more advanced techniques such as generalized auto-regression models and hidden Markov chains are presented and applied on a study on the activity of Rhipicephalus appendiculatus, the major vector of Theileria parva, a fatal disease in cattle. This study presents individual measurements on female R. appendiculatus, which are terminating their diapause (resting status) and become active. Comprehending activity patterns is very important to better understand the ecology of R. appendiculatus. The model indicates that activity and non-activity act in an absorbing way meaning that once a tick becomes active it shows a tendency to remain active. The change-point model estimates that the sudden change in activity happens on December 10. The reaction of ticks on acceleration and changes in rainfall and temperature indicates that ticks can sense climatic changes. The study revealed the underlying not visually observable states during diapause development of the adult tick of R. appendiculatus. These states could be related to phases during the dynamic event of diapause development and post-diapause activity in R. appendiculatus.
Notes: Inst Trop Med, B-2000 Antwerp, Belgium. Univ Maastricht, Dept Populat Genet, NL-6200 MD Maastricht, Netherlands. Limburgs Univ Centrum, Ctr Stat, B-3590 Diepenbeek, Belgium.Speybroeck, N, Inst Trop Med, Nationalestr 155, B-2000 Antwerp, Belgium.nspeybroeck@itg.be
URI: http://hdl.handle.net/1942/1979
DOI: 10.1007/s10651-005-5691-1
ISI #: 000235461900005
ISSN: 1352-8505
Category: A1
Type: Journal Contribution
Validation: ecoom, 2007
Appears in Collections: Research publications

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