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Prospects for predictive modeling of transition cow diseases

Part of: Big Data

Published online by Cambridge University Press:  16 September 2019

Lauren Wisnieski Open the ORCID record for Lauren Wisnieski [Opens in a new window] ,
Bo Norby ,
Steven J. Pierce ,
Tyler Becker  and
Lorraine M. Sordillo
Lauren Wisnieski*
Affiliation:
Center for Research Outcomes and Epidemiology, Kansas State University, 310 Coles Hall, Manhattan, KS66506, USA
Bo Norby
Affiliation:
Department of Large Animal Clinical Sciences, Michigan State University, 736 Wilson Rd, Room A-201, East Lansing, MI48824, USA
Steven J. Pierce
Affiliation:
Center for Statistical Training and Consulting, Michigan State University, 293 Farm Lane, Room 100A, East Lansing, MI48824, USA
Tyler Becker
Affiliation:
Department of Food Science and Human Nutrition, Michigan State University, 469 Wilson Rd, Room 125, East Lansing, MI48824, USA
Lorraine M. Sordillo
Affiliation:
Department of Large Animal Clinical Sciences, Michigan State University, 736 Wilson Rd, Room A-201, East Lansing, MI48824, USA
*
Author for correspondence: Lauren Wisnieski, E-mail: wisnies5@vet.k-state.edu
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Abstract

Transition cow diseases can negatively impact animal welfare and reduce dairy herd profitability. Transition cow disease incidence has remained relatively stable over time despite monitoring and management efforts aimed to reduce the risk of developing diseases. Dairy cattle disease risk is monitored by assessing multiple factors, including certain biomarker test results, health records, feed intake, body condition score, and milk production. However, these factors, which are used to make herd management decisions, are often reviewed separately without considering the correlation between them. In addition, the biomarkers that are currently used for monitoring may not be representative of the complex physiological changes that occur during the transition period. Predictive modeling, which uses data to predict future or current outcomes, is a method that can be used to combine the most predictive variables and their interactions efficiently. The use of an effective predictive model with relevant predictors for transition cow diseases will result in better targeted interventions, and therefore lower disease incidence. This review will discuss predictive modeling methods and candidate variables in the context of transition cow diseases. The next step is to investigate novel biomarkers and statistical methods that are best suited for the prediction of transition cow diseases.

Keywords

Disease predictionpredictive modelingstatistical methodstransition dairy cattle

Information

Type
Review Article
Information
Animal Health Research Reviews , Volume 20 , Issue 1 , June 2019 , pp. 19 - 30
Copyright
Copyright © Cambridge University Press 2019

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