Philip Rasmussen
University of Calgary
7 Papers
1 Citations
Philip Rasmussen is an academic researcher from University of Calgary. The author has contributed to research in topics: Paratuberculosis & Medicine. The author has an hindex of 1, co-authored 3 publications.
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Papers
Estimating the burden of multiple endemic diseases and health conditions using Bayes’ Theorem: A conditional probability model applied to UK dairy cattle
TL;DR: In this paper , the authors proposed a method to adjust an array of individual disease impact estimates so that they may be aggregated without overlap, which can be applied to any livestock system in any region with any set of endemic diseases.
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Global losses due to dairy cattle diseases: A comorbidity-adjusted economic analysis.
Philip Rasmussen,Herman W. Barkema,Prince P. Osei,James Taylor,A. P. Shaw,Beate Conrady,Gemma Chaters,Violeta Muñoz,David C. Hall,Ofosuhene O. Apenteng,Jonathan Rushton,Paul R. Torgerson +11 more
TL;DR: Global losses due to dairy cattle diseases are estimated to be USD 65 billion annually, with subclinical ketosis, clinical mastitis, and subclinical mastitis being the costliest diseases.
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Effectiveness and Economic Viability of Johne's Disease (Paratuberculosis) Control Practices in Dairy Herds
TL;DR: In this article, a Markovian framework using region-specific economic variables was used to investigate the effectiveness of vaccination and the relative economic impact of within-herd prevalence of paratuberculosis in dairy farms.
Estimation of the value of Johne's disease (paratuberculosis) control to Canadian dairy producers.
TL;DR: In this paper, the authors used confidential cost-of-production data from the Canadian Dairy Commission to develop a Canadian dairy production model incorporating feed, land, labor, and machinery.
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Economic premiums associated with Mycobacterium avium subspecies paratuberculosis-negative replacement purchases in major dairy-producing regions.
TL;DR: In this paper, the authors used Markov Chain Monte Carlo simulation techniques to compare two sets of MAP-negative and MAP-positive herds across a comprehensive selection of regions: herds purchasing MAPnegative replacement animals and herds purchasing replacement animals with unknown MAP infection status.
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