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
Standard
Application of a Bayesian hidden Markov model to determine dairy cattle herd status and test characteristics from Salmonella Dublin national surveillance data. / Meletis, Eleftherios; Nielsen, Liza Rosenbaum; Madouasse , Aurélien ; Conrady, Beate.
Society for Veterinary Epidemiology and Preventive Medicine, Proceedings: Belfast, Northern Ireland, 23-25 March 2022. Society for Veterinary Epidemiology and Preventive Medicine, 2023. p. 11.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Application of a Bayesian hidden Markov model to determine dairy cattle herd status and test characteristics from Salmonella Dublin national surveillance data
AU - Meletis, Eleftherios
AU - Nielsen, Liza Rosenbaum
AU - Madouasse , Aurélien
AU - Conrady, Beate
PY - 2023
Y1 - 2023
N2 - 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).
AB - 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).
M3 - Article in proceedings
SN - 978-0-948073-70-0
SP - 11
BT - Society for Veterinary Epidemiology and Preventive Medicine, Proceedings
PB - Society for Veterinary Epidemiology and Preventive Medicine
Y2 - 22 March 2022 through 25 March 2022
ER -
ID: 339259990