Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-18T23:54:43.664Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating somatic cell scores with a Bayesian Gaussian linear state-space model

Published online by Cambridge University Press:  06 January 2014

J. Detilleux ,
L. Theron ,
E. Reding ,
C. Bertozzi  and
C. Hanzen
J. Detilleux*
Affiliation:
Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium
L. Theron
Affiliation:
Large Animal Clinic, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium
E. Reding
Affiliation:
Association Wallonne de l’Elevage, 4 rue de Champs Elysées, 5590 Ciney, Belgium
C. Bertozzi
Affiliation:
Association Wallonne de l’Elevage, 4 rue de Champs Elysées, 5590 Ciney, Belgium
C. Hanzen
Affiliation:
Large Animal Clinic, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium
*
†E-mail: jdetilleux@ulg.ac.be
Get access

Abstract

Because accurate characterization of health state is important for managing dairy herds, we propose to validate the use of a linear state-space model (LSSM) for evaluating monthly somatic cell scores (SCSs). To do so, we retrieved SCS from a dairy database and collected reports on clinical mastitis collected in 20 farms, during the period from January 2008 to December 2011 in the Walloon region of Belgium. The dependent variable was the SCS, and the independent variables were the number of days from calving, year of calving and parity. The LSSM also incorporated an error-free underlying variable that described the trend across time as a function of previous clinical and subclinical status. We computed the mean sum of squared differences between observed SCS and median values of the posterior SCS distribution and constructed the receiver operating characteristic (ROC) curve for SCS thresholds going from 0 to 6. Our results show SCS estimates are close to observed SCS and area under the ROC curve is higher than 90%. We discuss the meaning of the parameters in light of our current knowledge of the disease and propose methods to incorporate, in LSSM, this knowledge often expressed in the form of ordinary differential equations.

Keywords

bovine mastitislinear state-space modelsomatic cell count
Type
Full Paper
Information
animal , Volume 8 , Issue 3 , March 2014 , pp. 477 - 483
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ali, A and Shook, GE 1980. An optimum transformation for somatic cell concentration in milk. Journal of Dairy Sciences 63, 487–490.CrossRefGoogle Scholar
Bradley, A and Green, M 2005. Use and interpretation of somatic cell count data in dairy cows. In Practice 27, 310–315.Google Scholar
Bradley, AJ, Leach, KA, Breen, JE, Green, LE and Green, MJ 2007. Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales. Veterinary Record 160, 253–257.Google Scholar
Chen, Z and Brown, EN 2013. State space models. Scholarpedia 8, 30868.Google Scholar
De Haas, Y, Veerkamp, RF, Barkema, HW, Grohn, YT and Schukken, YH 2004. Associations between pathogen-specific cases of clinical mastitis and somatic cell count patterns. Journal of Dairy Sciences 87, 95–105.Google Scholar
Detilleux, J and Leroy, PL 2000. Application of a mixed normal mixture model for the estimation of Mastitis-related parameters. Journal of Dairy Sciences 83, 2341–2349.Google Scholar
Detilleux, J, Theron, L, Beduin, JM and Hanzen, C 2012. A structural equation model to evaluate direct and indirect factors associated with a latent measure of mastitis in Belgian dairy herds. Preventive Veterinary Medicine 107, 170–179.CrossRefGoogle ScholarPubMed
Detilleux, JC 2011. A hidden Markov model to predict early mastitis from test-day somatic cell scores. Animal 5, 175–181.Google Scholar
Faye, B, Dorr, N, Lescourret, F, Barnouin, J and Chassagne, M 1994. Farming practices associated with the 'udder infection' complex. Veterinary Research 25, 213–218.Google Scholar
Gelman, A and Rubin, DB 1996. Markov chain Monte Carlo methods in biostatistics. Statistical Methods in Medical Research 5, 339–355.Google Scholar
Gianneechini, R, Concha, C, Rivero, R, Delucci, I and Moreno Lopez, J 2002. Occurrence of clinical and sub-clinical mastitis in dairy herds in the West Littoral Region in Uruguay. Acta Veterinary Scandinavia 43, 221–230.Google Scholar
Hall, IP 2013. Stratified medicine: drugs meet genetics. European Respiratory Review 22, 53–57.Google Scholar
Harmon, RJ 1994. Physiology of mastitis and factors affecting somatic cell counts. Journal of Dairy Sciences 77, 2103–2112.CrossRefGoogle ScholarPubMed
Hojsgaard, S and Friggens, NC 2010. Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows. Journal of Dairy Sciences 93, 582–592.Google Scholar
Hooker, G, Ellner, SP, Roditi Lde, V and Earn, DJ 2011. Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario. Journal of the Royal Society Interface 8, 961–974.CrossRefGoogle ScholarPubMed
Hovinen, M and Pyorala, S 2011. Invited review: udder health of dairy cows in automatic milking. Journal of Dairy Sciences 94, 547–562.Google Scholar
Jamrozik, J and Schaeffer, LR 2010. Application of multiple-trait finite mixture model to test-day records of milk yield and somatic cell score of Canadian Holsteins. Journal of Animal Breeding and Genetics 127, 361–368.Google Scholar
Klaas, IC, Enevoldsen, C, Vaarst, M and Houe, H 2004. Systematic clinical examinations for identification of latent udder health types in Danish dairy herds. Journal of Dairy Sciences 87, 1217–1228.Google Scholar
Kristula, MA, Curtis, CR, Galligan, DT and Bartholomew, RC 1992. Use of a repeated-measures logistic regression model to predict chronic mastitis in dairy cows. Preventive Veterinary Medicine 14, 54–68.Google Scholar
Laevens, H, Deluyker, H, Schukken, YH, De Meulemeester, L, Vandermeersch, R, De Muelenaere, E and De Kruif, A 1997. Influence of parity and stage of lactation on the somatic cell count in bacteriologically negative dairy cows. Journal of Dairy Sciences 80, 3219–3226.Google Scholar
Lievaart, JJ, Kremer, WD and Barkema, HW 2007. Short communication: comparison of bulk milk, yield-corrected, and mean somatic cell counts as parameters to summarize the subclinical mastitis situation in a dairy herd. Journal of Dairy Sciences 90, 4145–4148.Google Scholar
Lunn, D, Spiegelhalter, D, Thomas, A and Best, N 2009. The BUGS project: evolution, critique and future directions. Statistics in Medicine 28, 3049–3067.Google Scholar
Metz, CE, Herman, BA and Shen, JH 1998. Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Statistics in Medicine 17, 1033–1053.Google Scholar
Miltenburg, JD, de Lange, D, Crauwels, AP, Bongers, JH, Tielen, MJ, Schukken, YH and Elbers, AR 1996. Incidence of clinical mastitis in a random sample of dairy herds in the southern Netherlands. Veterinary Record 139, 204–207.Google Scholar
Mork, M, Lindberg, A, Alenius, S, Vagsholm, I and Egenvall, A 2009. Comparison between dairy cow disease incidence in data registered by farmers and in data from a disease-recording system based on veterinary reporting. Preventive Veterinary Medicine 88, 298–307.Google Scholar
Morris, AP, Lindgren, CM, Zeggini, E, Timpson, NJ, Frayling, TM, Hattersley, AT and McCarthy, MI 2010. A powerful approach to sub-phenotype analysis in population-based genetic association studies. Genetic Epidemiology 34, 335–343.CrossRefGoogle ScholarPubMed
Osteras, O, Solbu, H, Refsdal, AO, Roalkvam, T, Filseth, O and Minsaas, A 2007. Results and evaluation of thirty years of health recordings in the Norwegian dairy cattle population. Journal of Dairy Sciences 90, 4483–4497.CrossRefGoogle ScholarPubMed
Pighetti, GM and Elliott, AA 2011. Gene polymorphisms: the keys for marker assisted selection and unraveling core regulatory pathways for mastitis resistance. Journal of Mammary Gland Biology and Neoplasia 16, 421–432.CrossRefGoogle ScholarPubMed
Quach, M, Brunel, N and d'Alche-Buc, F 2007. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics 23, 3209–3216.CrossRefGoogle ScholarPubMed
Robertson, C, Sawford, K, Gunawardana, WS, Nelson, TA, Nathoo, F and Stephen, C 2011. A hidden Markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance. PLoS One 6, e24833.Google Scholar
Reding, E, Theron, L, Detilleux, J, Bertozzi, C and Hanzen, C 2012. LAECEA: un outil fédérateur d’aide à la décision pour le suivi de la santé mammaire dans les élevages bovins laitiers wallons. In the 18th annual meeting of the 3 R. Session Aide à la décision en élevage. Paris, France.Google Scholar
Roweis, S and Ghahramani, Z 1999. A unifying review of linear gaussian models. Neural Computing 11, 305–345.Google Scholar
Schepers, AJ, Lam, TJ, Schukken, YH, Wilmink, JB and Hanekamp, WJ 1997. Estimation of variance components for somatic cell counts to determine thresholds for uninfected quarters. Journal of Dairy Sciences 80, 1833–1840.Google Scholar
Soyeurt, H, Bastin, C, Colinet, FG, Arnould, VM, Berry, DP, Wall, E, Dehareng, F, Nguyen, HN, Dardenne, P, Schefers, J, Vandenplas, J, Weigel, K, Coffey, M, Theron, L, Detilleux, J, Reding, E, Gengler, N and McParland, S 2012. Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal 6, 1830–1838.Google Scholar
Steeneveld, W, Hogeveen, H, Barkema, HW, van den Broek, J and Huirne, RB 2008. The influence of cow factors on the incidence of clinical mastitis in dairy cows. Journal of Dairy Sciences 91, 1391–1402.Google Scholar
Uhler, C 2009. Mastitis in dairy production: estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard. Journal of Agricultural, Biological, and Environmental Statistics 14, 79–98.Google Scholar
White, LJ, Schukken, YH, Dogan, B, Green, L, Dopfer, D, Chappell, MJ and Medley, GF 2010. Modelling the dynamics of intramammary E. coli infections in dairy cows: understanding mechanisms that distinguish transient from persistent infections. Veterinary Research 41, 13–21.Google Scholar
Wiggs, JL 2010. Genotypes need phenotypes. Archives of Ophthalmology 128, 934–935.Google Scholar
Wolfova, M, Stipkova, M and Wolf, J 2006. Incidence and economics of clinical mastitis in five Holstein herds in the Czech Republic. Preventive Veterinary Medicine 77, 48–64.Google Scholar