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A Probabilistic Transmission Model of Salmonella in the Primary Broiler Production Chain

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  • Jukka Ranta
  • Riitta Maijala

Abstract

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%–17.4%] (no intervention), and [0.9%–5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%–43.1%] and [1.0%–5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.

Suggested Citation

  • Jukka Ranta & Riitta Maijala, 2002. "A Probabilistic Transmission Model of Salmonella in the Primary Broiler Production Chain," Risk Analysis, John Wiley & Sons, vol. 22(1), pages 47-58, February.
    • Handle: RePEc:wly:riskan:v:22:y:2002:i:1:p:47-58
    DOI: 10.1111/0272-4332.t01-1-00004
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    References listed on IDEAS

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    1. Denis Mollison & Valerie Isham & Bryan Grenfell, 1994. "Epidemics: Models and Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(1), pages 115-129, January.
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    Cited by:

    1. Antti Mikkelä & Jukka Ranta & Pirkko Tuominen, 2019. "A Modular Bayesian Salmonella Source Attribution Model for Sparse Data," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1796-1811, August.
    2. Winy Messens & Luis Vivas-Alegre & Saghir Bashir & Giusi Amore & Pablo Romero-Barrios & Marta Hugas, 2013. "Estimating the Public Health Impact of Setting Targets at the European Level for the Reduction of Zoonotic Salmonella in Certain Poultry Populations," IJERPH, MDPI, vol. 10(10), pages 1-15, October.
    3. Tatiana V. Kotsofana, 2018. "The Driving Forces Of Inflation And The Mechanisms For Their Implementation In The Russian Economy," Annals of marketing-mba, Department of Marketing, Marketing MBA (RSconsult), vol. 1, April.
    4. Maarten Nauta & Ine Van Der Fels‐Klerx & Arie Havelaar, 2005. "A Poultry‐Processing Model for Quantitative Microbiological Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 25(1), pages 85-98, February.
    5. Kangas, Susanna & Lyytikainen, Tapani & Peltola, Jukka & Ranta, Jukka & Maijala, Riitta, 2004. "Costs and Benefits of Two Alternative Salmonella Control Policies in Finnish Broiler Production," 84th Seminar, February 8-11, 2004, Zeist, The Netherlands 24981, European Association of Agricultural Economists.
    6. Carolina Plaza Rodríguez & Guido Correia Carreira & Annemarie Käsbohrer, 2018. "A Probabilistic Transmission Model for the Spread of Extended‐Spectrum‐β‐Lactamase and AmpC‐β‐Lactamase‐Producing Escherichia Coli in the Broiler Production Chain," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2659-2682, December.

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