Practical Application of Epidemiology in Veterinary Herd Health/production Medicine

Booker, Calvin W.; Loneragan, Guy H.; Guichon, P T; Jim, G. K.; Schunicht, O. C.; Wildman, Brian K.; Pittman, T. J.; Fenton, R. Kent; Janzen, E. D.; Perrett, Tye; Smith, R. A.

doi:10.21423/aabppro20044902

Cited by 2 publications

(1 citation statement)

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“…Bovine respiratory disease (BRD) is one of the costliest diseases in the feedlot industry ( Salman et al, 1991 ). Early identification and prompt treatment of the disease can reduce the costs associated with BRD ( Booker et al, 2004 ). Additionally, gastrointestinal diseases, lameness, and other adverse health events negatively impact animal performance, but the prevalence of these other diseases is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of predictive models to determine total morbidity outcome of feedlot cattle based on cohort-level feed delivery data during the first 15 days on feed

Heinen

Lancaster

White

2022

Translational Animal Science

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Changes in feeding behavior and intake have been used to predict onset of bovine respiratory disease in individual animals but have not been applied to cohort level data. Correctly identifying high morbidity cohorts of cattle early in the feeding period could facilitate the administration of interventions to improve health and economic outcomes. The study objective was to determine the ability of feed delivery data from the first 15 days on feed to predict total feeding period morbidity. Data consisted of 518 cohorts (10 feedlots, 56,796 animals) of cattle of varying sex, age, arrival weight, and arrival time of year over a 2-year period. Overall cohort-level morbidity was classified into high (≥15% total morbidity) or low categories with 18.5% of cohorts having high morbidity. Five predictive models (advanced perceptron, decision forest, logistic regression, neural network, and boosted decision tree) were created to predict overall morbidity given cattle characteristics at arrival and feeding characteristics from the first 15 days. The dataset was split into training and testing subsets (75% and 25% of original, respectively), stratified by outcome of interest. Predictive models were generated in Microsoft Azure using the training set and overall predictive performance was evaluated using the testing set. Performance in the testing set (n=130) was measured based on final accuracy, sensitivity (Sn, the ability to accurately detect high morbidity cohorts), and specificity (Sp, the ability to accurately detect low morbidity cohorts). The decision forest had the highest Sp (97%) with the greatest ability to accurately identify low morbidity lots (103 of 106 identified correctly), but this model had low Sn (33%). The logistic regression and neural network had similar Sn (both 63%) and Sp (69 and 72%, respectively) with the best ability to correctly identify high morbidity cohorts (15 of 24 correctly identified). Predictor variables with the greatest importance in the predictive models included percent change in feed delivery between days and 4-day moving averages. The most frequent variable with a high level of importance among models was the percent change in feed delivered from d 2 to 3 after arrival. In conclusion, feed delivery data during the first 15 days on feed was a significant predictor of total cohort-level morbidity over the entire feeding period with changes in feed delivery providing important information.

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Section: Introductionmentioning

confidence: 99%