Economic impact of mortality prediction by predictive model at first and second treatment for bovine respiratory disease.

OBJECTIVE: To evaluate a predictive model's ability to determine cattle mortality following first and second treatment for bovine respiratory disease and to understand the differences in net returns comparing predictive models to the status quo. METHODS: 2 boosted decision tree models were constructed, 1 using data known at first treatment and 1 with data known at second treatment. Then, the economic impact of each outcome (true positive, true negative, false positive, and false negative) was estimated using various market values to determine the net return per head of using the predictive model to determine which animals should be culled at treatment. This was compared to the status quo to determine the difference in net return. RESULTS: The models constructed for the prediction of mortality performed with moderate accuracy (areas under the curve > 0.7). The economic analysis found that the models at a high specificity (> 90%) could generate a positive net return in comparison to status quo. CONCLUSIONS: This study showed that predictive models may be a useful tool to make culling decisions and could result in positive net returns. CLINICAL RELEVANCE: Bovine respiratory disease is the costliest health condition experienced by cattle on feed. Feedyard record-keeping systems generate vast amounts of data that could be used in predictive models to make management decisions. It is essential to understand the accuracy of predictions made via machine learning. However, the economic impact of implementing predictive models in a feedyard will influence adoption.

Determining the Economically Optimum Metaphylactic Strategy for Cattle Cohorts of Varied Demographic Characteristics.

Metaphylactic antibiotic use in feeder cattle is a common practice to control respiratory disease. Antimicrobial stewardship is important to ensure continued efficacy and to protect animal welfare. The objective of this study is to identify characteristics of cohorts of cattle that had not received metaphylaxis that would have benefited economically from the use of metaphylaxis. Cohorts (n = 12,785; 2,206,338 head) from 13 feedlots that did not receive metaphylaxis were modeled using an economic model to estimate net returns for three metaphylactic options. Logistic regression models with covariates for entry weight, sex, average daily weight gain, number of animals per cohort, and days on feed, with feedlot as a random effect, were used to determine the model-adjusted probability of cohorts benefiting economically from metaphylaxis. Most (72%) cohorts in this data set that had not received metaphylaxis at arrival would not economically benefit from metaphylaxis. Sex, entry weight category, number of cattle in the cohort, and average daily weight gain were associated with the likelihood of benefitting economically from metaphylaxis. The results illustrated that cattle cohort demographics influenced the probability that cohorts would benefit economically from metaphylaxis and the type of metaphylaxis utilized, and integrating this information has the potential to influence the metaphylaxis decision.