Application of a Bayesian hidden Markov model to determine dairy cattle herd status and test characteristics from Salmonella Dublin national surveillance data
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
The aims were twofold. First, to develop a statistical model predicting the probabilities of freedom from Salmonella enterica subspecies enterica serovar Dublin (S. Dublin) antibodies in Danish dairy herds using longitudinal herd-level surveillance data. These data were obtained via antibody-detecting ELISA testing on bulk tank milk (BTM), four times a year. Second, to estimate the sensitivity (Se) and specificity (Sp) of the testing procedure. A total of 8,310 BTM test results from 500 Danish dairy herds were used for this study between 2017 and 2022. These data were combined with prior distributions in a Bayesian Hidden Markov Model ('the STOC free model') to predict herd-level probabilities of antibody presence (and freedom from antibodies). The model fitted the S. Dublin data and gave meaningful results when compared to previous studies and other types of models, i.e., Se = 0.945 (95% credible interval (CI): 0.921-0.962) and Sp = 0.995 (95% CI: 0.992-0.997).
Original language | English |
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Title of host publication | Society for Veterinary Epidemiology and Preventive Medicine, Proceedings : Belfast, Northern Ireland, 23-25 March 2022 |
Number of pages | 17 |
Publisher | Society for Veterinary Epidemiology and Preventive Medicine |
Publication date | 2023 |
Pages | 11 |
ISBN (Print) | 978-0-948073-70-0 |
Publication status | Published - 2023 |
Event | SVEPM 2022 - Belfast, Belfast, United Kingdom Duration: 22 Mar 2022 → 25 Mar 2022 |
Conference
Conference | SVEPM 2022 |
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Location | Belfast |
Land | United Kingdom |
By | Belfast |
Periode | 22/03/2022 → 25/03/2022 |
ID: 339259990