The control of human Campylobacteriosis is a priority in public health agendas all over the world. Poultry is considered a significant risk factor for human infections with
Campylobacter and risk assessment models indicate that the successful implementation of
Campylobacter control strategies in poultry will translate on a reduction of human Campylobacteriosis cases. Efficient control strategies implemented during primary production will reduce the risk of
Campylobacter introduction in chicken houses and/or decrease
Campylobacter concentration in infected chickens and their products. Consequently, poultry producers need to make difficult decisions under conditions of uncertainty regarding the implementation of
Campylobacter control strategies. This manuscript presents the development of probabilistic graphical models to support decision making in order to control
Campylobacter in poultry. The decision support systems are constructed as probabilistic graphical models (PGMs) which integrate knowledge and use Bayesian methods to deal with uncertainty. This paper presents a specific model designed to integrate epidemiological knowledge from the United Kingdom (UK model) in order to assist poultry managers in specific decisions related to vaccination of commercial broilers for the control of
Campylobacter. Epidemiological considerations and other crucial aspects including challenges associated with the quantitative part of the models are discussed in this manuscript. The outcome of the PGMs will depend on the qualitative and quantitative data included in the models. Results from the UK model and sensitivity analyses indicated that the financial variables (cost/reward functions) and the effectiveness of the control strategies considered in the UK model were driving the results. In fact, there were no or only small financial gains when using a hypothetical vaccine B (able to decrease
Campylobacter numbers from two to six logs in 20% of the chickens with a cost of 0.025 £/chicken) and reward system 1 (based on similar gross profits in relation to
Campylobacter levels) under the specific assumptions considered in the UK model. In contrast, significant reductions in expected
Campylobacter numbers and substantial associated expected financial gains were obtained from this model when considering the reward system 2 (based on quite different gross profits in relation to
Campylobacter levels) and the use of a hypothetical cost-effective vaccine C (able to reduce the level of
Campylobacter from two to six logs in 90% of the chickens with a cost of 0.03 £/chicken). The flexibility of probabilistic graphical models allows for the inclusion of more than one
Campylobacter vaccination strategy and more than one reward system and consequently, diverse potential solutions for the control of
Campylobacter may be considered. Cost-effective
Campylobacter control strategies that can significantly reduce the probability of
Campylobacter introduction into a flock and/or the numbers of
Campylobacter in already infected chickens, and translate to an attractive cost-reward balance will be preferred by poultry producers.
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