Decision tree analysis for pathogen identification based on circumstantial factors in outbreaks of bovine respiratory...

Respiratory tract infections continue to be a leading cause of economic loss, hampered animal welfare and intensive antimicrobial use in cattle operations, worldwide. To better target antimicrobial therapy, control and prevention towards the involved pathogens, there is a growing interest in microbiological tests on respiratory samples. However, these tests are time consuming, cost money and sampling might compromise animal welfare. Therefore, the objective of the present study was to develop immediately applicable decision trees for pathogen identification in outbreaks of bovine respiratory disease based on circumstantial factors. Data from a cross sectional study, involving 201 outbreaks of bovine respiratory disease in dairy and beef farms between 2016 and 2019 was used. Pathogens were identified by a semi-quantitative PCR (polymerase chain reaction) on a pooled non-endoscopic broncho-alveolar lavage sample from clinically affected animals. Potential risk factors of involved animals, environment, management and housing were obtained by enquiry. Classification and regression tree analysis was used for decision tree development with cross-validation. Different trees were constructed, involving a general 3-group classification tree (viruses, Mycoplasma bovis or Pasteurellaceae family) and a tree for each single pathogen. The general 3- group classification tree was 52.7 % accurate and had a sensitivity of 81.5 % and a specificity 52.2 % for viruses, respectively 51.7 % and 84.4 % for M. bovis and 28.9 % and 93.6 % for Pasteurellaceae. The single-pathogen trees were more specific than sensitive: Histophilus somni (Se = 25.8 %; Sp = 94.5 %), Mannheimia haemolytica (Se = 69.2 %; Sp = 70.6 %), bovine coronavirus (Se = 42.2 %; Sp = 89.6 %) and bovine respiratory syncytial virus (Se = 34.0 %; Sp = 96.6 %). For Pasteurella multocida, M. bovis and parainfluenzavirus type 3 no meaningful tree was obtained. The concept and trees are promising, but currently lack sensitivity and specificity in order to be a reliable tool for practice. For now, the obtained trees can already be informative for decision making to some extend depending on the end node in which an outbreak falls.