Contact structure and Salmonella control in the network of pig movements in France.

Movement restrictions are a key measure to control pathogen transmission in the swine industry due when purchasing animals. Here recorded animal movements between herds in France are used to generate a network model. This network consists of different types of herds together with specific links between them, with some herds being suppliers (of reproductive gilts or piglets) for others. The connectivity between herds within this network exhibits a highly variable number of contacts according to the type of animal moved (8 kg piglets, 25 kg piglets or reproductive gilts). A simulation model is used to assess the impact of control measures aimed at reducing Salmonella prevalence at every level of the production network. In particular we consider restricting animal purchases according to the infectious status of herds (level of prevalence) such that animals do not move from higher to lower herd prevalence; this measure can additionally be combined with infection control at the herd-level. When Salmonella initially invades the production structure, the restriction of animal movements alone is sufficient to significantly decrease the number of herds infected. In contrast, once infection is endemic within the production structure, movement restrictions have to be supplemented by within-herd control applied to a large proportion of herds to reduce the pathogen prevalence. These results demonstrate the important distinction between the control of epidemics and endemic infections and highlight the different impacts of control measures at the within- and at the between-herd levels.

Sensitivity analysis to identify key parameters influencing Salmonella infection dynamics in a pig batch.

In the context of managed herds, epidemiological models usually take into account relatively complex interactions involving a high number of parameters. Some parameters may be uncertain and/or highly variable, especially epidemiological parameters. Their impact on the model outputs must then be assessed by a sensitivity analysis, allowing to identify key parameters. The prevalence over time is an output of particular interest in epidemiological models, so sensitivity analysis methods adapted to such dynamic output are needed. In this paper, such a sensitivity analysis method, based on a principal component analysis and on analysis of variance, is presented. It allows to compute a generalised sensitivity index for each parameter of a model representing Salmonella spread within a pig batch. The model is a stochastic discrete-time model describing the batch dynamics and movements between rearing rooms, from birth to slaughterhouse delivery. Four health states were introduced: Salmonella-free, seronegative shedder, seropositive shedder and seropositive carrier. The indirect transmission was modelled via an infection probability function depending on the quantity of Salmonella in the rearing room. Simulations were run according to a fractional factorial design enabling the estimation of main effects and two-factor interactions. For each of the 18 epidemiological parameters, four values were chosen, leading to 4096 scenarios. For each scenario, 15 replications were performed, leading to 61440 simulations. The sensitivity analysis was then conducted on the seroprevalence output. The parameters governing the infection probability function and residual room contaminations were identified as key parameters. To control the Salmonella seroprevalence, efficient measures should therefore aim at these parameters. Moreover, the shedding rate and maternal protective factor also had a major impact. Therefore, further investigation on the protective effect of maternal or post-infection antibodies would be needed.

Modelling Salmonella spread within a farrow-to-finish pig herd.

Delivery of infected pigs to the slaughterhouse is a major source of pork meat contamination by bacterial hazards to humans. We propose a model of Salmonella spread within a farrow-to-finish pig herd, assuming the prevalence in infected delivered pigs depends on the whole pig life-time and growing process. This stochastic discrete-time model represents both the population dynamics in a farrow-to-finish pig herd using batch management, and Salmonella spread. Four mutually exclusive individual health states were considered: Salmonella-free, seronegative shedder, seropositive shedder and seropositive not shedding carrier, making the distinction between seropositive animals and shedders. Since indirect transmission is the main route of transmission, the probability of infection depends on the quantity of Salmonella in the pigs' environment (Q). A dose effect function is used with two thresholds, assuming saturation in exposure for high Q vs. a minimum exposure for low Q. Salmonella is introduced in an initially Salmonella-free 150-sow herd. Prevalence of shedders and seroprevalence are calculated over time in batches of sows and pigs, and in groups of delivered pigs, composed of pigs from different batches. The model shows very variable seroprevalence over time within a herd among delivered groups, as well as among replications. The mean seroprevalence and the mean shedding prevalence are 19.3% and 13.8% respectively. A sensitivity analysis shows that the Salmonella quantity shed and the maternal protective factor are the most influential parameters on Salmonella prevalence in delivered pigs.