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

Lowie, Thomas; Callens, Jozefien; Maris, Jo; Ribbens, Stefaan; Pardon, Bart

doi:10.1016/j.prevetmed.2021.105469

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…28 Despite viruses being frequently involved in BRD across the study region, at present, no reliable methods are available to differentiate between viral pneumonia or upper respiratory tract infection and bacterial pneumonia at first instance. 26,29 BRD, and in particular pneumonia, results in significant economic losses and increases the risk of mortality or premature culling. 30,31 For these reasons, and considering the long turnaround time between sampling and availability of laboratory test results, many veterinarians, possibly under pressure from their clients, are likely to prefer antimicrobial coverage in BRD outbreaks.…”

Section: Discussionmentioning

confidence: 99%

“…BRD is a multifactorial infectious disease, caused by a complex interaction between different viral and bacterial pathogens, that may manifest from an upper respiratory tract infection to a life‐threatening pneumonia 28 . Despite viruses being frequently involved in BRD across the study region, at present, no reliable methods are available to differentiate between viral pneumonia or upper respiratory tract infection and bacterial pneumonia at first instance 26,29 . BRD, and in particular pneumonia, results in significant economic losses and increases the risk of mortality or premature culling 30,31 .…”

Section: Discussionmentioning

confidence: 99%

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Cross‐sectional study of primary antimicrobial treatment and vaccination coverage in outbreaks of bovine respiratory disease on dairy and beef farms in northern Belgium

et al. 2022

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Background To what extent veterinarians active in the dairy or beef sector follow the antimicrobial therapy guidelines made available in different European countries for bovine respiratory disease (BRD) outbreaks, and whether differences in therapeutic or preventive preferences for BRD management exist, is currently unknown. Therefore, the objectives of this cross‐sectional study were to compare vaccination coverage and primary antimicrobial and anti‐inflammatory treatment on dairy, beef and mixed‐breed farms in northern Belgium, and determine their compliance with the recommendations made by the Belgian formulary. Methods Information on antimicrobial and anti‐inflammatory drug use and vaccination coverage from 190 BRD outbreaks in 180 herds, submitted by 101 veterinarians, was analysed. Multivariable linear probability models, adjusted for clustering at the veterinarian level, were used to determine differences between dairy and beef farms. Results Antimicrobials and non‐steroidal anti‐inflammatory drugs (NSAIDs) were used in 93.5% and 81.7% of the BRD outbreaks, respectively. First‐line antimicrobials were used as primary treatment in only 42.3%, 50.9% and 38.6% of dairy, beef and mixed‐breed farms, respectively. Significant differences (p < 0.05) were observed between dairy and beef farms in terms of use of long‐acting macrolides (–17.2 percentage points [pp]; 95% confidence interval [CI]: –31.9, –2.5), steroidal anti‐inflammatory drugs (15.2 pp; CI: 0.5, 29.8) and vaccination coverage (bovine respiratory syncytial virus, parainfluenza virus type 3 [33.1 pp; Cl: 15.7–50.6] and Mannheimia haemolytica [23.1 pp; Cl: 3.4–39.8]). Limitations The herds that participated in this study were likely among the more motivated regarding BRD control. As such, the information on vaccination coverage is likely not entirely representative of herds in the study area. Interpretation is further complicated by the fact that vaccinated herds were potentially less likely to face a BRD outbreak and therefore participate in the current study. Conclusion This study reveals differences in the primary use of (N)SAIDs, type of antimicrobials used and vaccination coverage on beef and dairy farms in the study region, and also differences in the appropriateness of antimicrobial selection based on the Belgian formulary.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cross‐sectional study of primary antimicrobial treatment and vaccination coverage in outbreaks of bovine respiratory disease on dairy and beef farms in northern Belgium

et al. 2022

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“…At the same time, we searched for clinical profiles by applying different learning techniques [17,24] through supervised and unsupervised analysis to identify the best possible classification grouping fit for the data, without any loss of relevant information. Based on their internal and visual characteristics, various group profiles were generated.…”

Section: Methodsmentioning

confidence: 99%

“…As previously mentioned, there are different cluster analysis methods based on hierarchies [24,25]. For this reason, after the implementation of each hierarchical technique, using normalized data and the Euclidean distance measure, the cophenetic correlation coefficients between the distance matrix and the cophenetic matrix were calculated for each of them.…”

Section: Unsupervised Cluster Analysismentioning

confidence: 99%

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Boukichou-Abdelkader,

Montero-Alonso,

Muñoz-García

2024

Computation

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Recently, many methods and algorithms have been developed that can be quickly adapted to different situations within a population of interest, especially in the health sector. Success has been achieved by generating better models and higher-quality results to facilitate decision making, as well as to propose new diagnostic procedures and treatments adapted to each patient. These models can also improve people’s quality of life, dissuade bad health habits, reinforce good habits, and modify the pre-existing ones. In this sense, the objective of this study was to apply supervised and unsupervised classification techniques, where the clustering algorithm was the key factor for grouping. This led to the development of three optimal groups of clinical pattern based on their characteristics. The supervised classification methods used in this study were Correspondence (CA) and Decision Trees (DT), which served as visual aids to identify the possible groups. At the same time, they were used as exploratory mechanisms to confirm the results for the existing information, which enhanced the value of the final results. In conclusion, this multi-technique approach was found to be a feasible method that can be used in different situations when there are sufficient data. It was thus necessary to reduce the dimensional space, provide missing values for high-quality information, and apply classification models to search for patterns in the clinical profiles, with a view to grouping the patients efficiently and accurately so that the clinical results can be applied in other research studies.

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Spatio-Temporal Analysis in Open Dataset from Respiratory Diseases

Méndez

Huerta

Navarro

et al. 2022

Communications in Computer and Information Science

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

Cited by 9 publications

References 35 publications

Cross‐sectional study of primary antimicrobial treatment and vaccination coverage in outbreaks of bovine respiratory disease on dairy and beef farms in northern Belgium

Cross‐sectional study of primary antimicrobial treatment and vaccination coverage in outbreaks of bovine respiratory disease on dairy and beef farms in northern Belgium

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Spatio-Temporal Analysis in Open Dataset from Respiratory Diseases

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