Efficacy of statistical process control procedures to monitor deviations in physical behavior for preclinical detection of bovine respiratory disease in feedlot cattle

Opportunities exist to improve the efficacy of antimicrobial treatment and animal welfare standards through use of remote sensor technologies for early detection of bovine respiratory disease (BRD). A post-hoc analysis using statistical process control (SPC) procedures was performed on continuously-recorded physical activity data collected from BRD-diagnosed and healthy calves from Pillen et al. (2016). We hypothesized that SPC models that monitor physical activity traits (step count, motion index, standing time, lying bouts) would yield higher diagnostic accuracies compared to methods based on visual observation of clinical signs of illness for detecting the onset of BRD. Crossbred steers and bulls (n = 266; initial BW = 180 kg) at high risk for BRD were fitted with leg-attached accelerometers (IceQube, IceRobotics, Ltd.) upon arrival at a commercial feedlot and evaluated for 56 d Overall, calves experienced 48% morbidity, with the average day of first treatment occurring 16 d postfeedlot arrival. Shewhart charts were used to evaluate daily changes in each trait as univariate models and combined traits in principal component analysis (PCA)-constructed multivariate models, relative to the day of BRD diagnosis. Diagnostic test sensitivity and specificity were calculated for each model and compared. The univariate models had relatively low sensitivities (< 40%), with specificities ranging from 23 to 81%, and chart signaling occurred up to 2 d prior to visual diagnosis of BRD. The univariate model for step count had the highest accuracy (52.3%), while lying time had the lowest accuracy (32%). The multivariate models that included all 4 physical activity traits or only lying bout behavior had moderate sensitivities (44 to 57%), but low specificities (< 37%), and chart signaling occurred 2 d prior to visual diagnosis. The relatively low diagnostic test accuracies of the SPC models reported in this study may have been due to substantial within-animal daily variation in the physical activity traits and(or) the limited time for adequate model training prior to the onset of BRD cases. Future research should investigate SPC analysis of multifactorial algorithms using physical activity and feeding behavior traits to improve the accuracy of preclinical detection of BRD.