Use of a dynamic programming model to estimate the value of clinical mastitis treatment and prevention options utilized by dairy producers

Bar, Doron; Tauer, Loren W.; Bennett, Gary J.; González, R. N.; Hertl, J.A.; Schulte, H. F.; Schukken, Y.H.; Welcome, F.L.; Gröhn, Yrjö T.

doi:10.1016/j.agsy.2008.09.001

Cited by 17 publications

(13 citation statements)

References 18 publications

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“…Such an information level allows for pathogen specific effects on milk yield, conception, mortality, treatment cost, and risk of new cases, but those advantages must be weighed against increased culture costs to determine the pathogen. Instead of observing mastitis cases at pathogen level it is worth considering whether a cheaper test method only determining the Gram type should be applied instead as assumed in Cha et al (2011) or even completely without testing the type of mastitis as assumed by Bar et al (2008a).…”

Section: Full Observability Of State Spacementioning

confidence: 99%

“…The age is typically measured as parity (often supplemented by month of lactation), the milk yield as a number of discrete levels, and the mastitis variable may simply have the states "Yes" and "No" like in Bar et al (2008a), or it may be modeled at a more detailed level. Thus, Cha et al Based on the current state a decision is made as shown in the figure.…”

Section: Markov Decision Processesmentioning

confidence: 99%

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From biological models to economic optimization

Kristensen

2015

Preventive Veterinary Medicine

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This article addresses the additional challenges being faced when biological models are used as a basis for decision support in livestock herds. The challenges include dealing with uncertain information, observation costs, herd dynamics and methodological issues in relation to the computational methods applied particularly in the dynamic case. The desired key property of information included in models is that it can be used as the basis for unbiased prediction of the future performance of the animals. Often there will be a tradeoff between uncertainty and costs in the sense that the level of uncertainty can be reduced (for instance through additional tests) at some cost. Thus, the decision about which (and how many) tests to perform can be seen as an optimization problem in itself. Another way of expressing the tradeoff is to talk about the value of information which can sometimes be assessed by modeling different approaches and levels of detail in data collection. Various optimization methods of relevance to herd health management are discussed with the main emphasis on decision graphs in the static case and Markov decision processes (dynamic programming) in a dynamic context.

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Section: Full Observability Of State Spacementioning

confidence: 99%

Section: Markov Decision Processesmentioning

confidence: 99%

From biological models to economic optimization

Kristensen

2015

Preventive Veterinary Medicine

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“…With this tool we can assess the value of diagnostic information (above mentioned generic vs. Gram± vs. bacteria specific mastitis) and determine the value of intervention strategies (e.g., Bar et al, 2008c).…”

Section: Systems Approachmentioning

confidence: 99%

Progression to multi-scale models and the application to food system intervention strategies

Gröhn

2015

Preventive Veterinary Medicine

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“…Often these problems involve making the most efficient use of available resources including money, time, machinery, staff, inventory, and land and water. These problems have been solved by using different optimization techniques from the researchers from worldwide (Anwar and Clarke, 2001;Ayvaz and Karahan, 2008;Azaiez et al, 2005;Bar et al, 2009;Bardossy et al, 1990;Chiadamrong and Kawtummachai, 2008;de Vries and Anwar, 2006;Easa and Hossain, 2008;Hsu and Wei, 2007;Hsu et al, 2008;Hugo, 2004;Joubert et al, 2007;Kale et al, 2008;Karmakar and Mujumdar, 2006;Majone et al, 2010;Mattilal and Duley, 1998;Pathumnakul et al, 2011;Ravirala and Grivas, 1995;Sadeghpour et al, 2006;Salazar et al, 2005;Stray et al, 2012;Wei and Hsu, 2009;Zhang and Huang, 2011).…”

Section: Introductionmentioning

confidence: 99%