Control measures to prevent the increase of paratuberculosis prevalence in dairy cattle herds: an individual-based modelling approach

Paratuberculosis, a gastrointestinal disease caused by Mycobacterium avium subsp. paratuberculosis (Map), can lead to severe economic losses in dairy cattle farms. Current measures are aimed at controlling prevalence in infected herds, but are not fully effective. Our objective was to determine the most effective control measures to prevent an increase in adult prevalence in infected herds. We developed a new individual-based model coupling population and infection dynamics. Animals are characterized by their age (6 groups) and health state (6 states). The model accounted for all transmission routes and two control measures used in the field, namely reduced calf exposure to adult faeces and test-and-cull. We defined three herd statuses (low, moderate, and high) based on realistic prevalence ranges observed in French dairy cattle herds. We showed that the most relevant control measures depend on prevalence. Calf management and test-and-cull both were required to maximize the probability of stabilizing herd status. A reduced calf exposure was confirmed to be the most influential measure, followed by test frequency and the proportion of infected animals that were detected and culled. Culling of detected high shedders could be delayed for up to 3 months without impacting prevalence. Management of low prevalence herds is a priority since the probability of status stabilization is high after implementing prioritized measures. On the contrary, an increase in prevalence was particularly difficult to prevent in moderate prevalence herds, and was only feasible in high prevalence herds if the level of control was high

Which phenotypic traits of resistance should be improved in cattle to control paratuberculosis dynamics in a dairy herd: a modelling approach

Abstract Paratuberculosis is a worldwide disease causing production losses in dairy cattle herds. Variability of cattle response to exposure to Mycobacterium avium subsp. paratuberculosis (Map) has been highlighted. Such individual variability could influence Map spread at larger scale. Cattle resistance to paratuberculosis has been shown to be heritable, suggesting genetic selection could enhance disease control. Our objective was to identify which phenotypic traits characterising the individual course of infection influence Map spread in a dairy cattle herd. We used a stochastic mechanistic model. Resistance consisted in the ability to prevent infection and the ability to cope with infection. We assessed the effect of varying (alone and combined) fourteen phenotypic traits characterising the infection course. We calculated four model outputs 25 years after Map introduction in a naïve herd: cumulative incidence, infection persistence, and prevalence of infected and affected animals. A cluster analysis identified influential phenotypes of cattle resistance. An ANOVA quantified the contribution of traits to model output variance. Four phenotypic traits strongly influenced Map spread: the decay in susceptibility with age (the most effective), the quantity of Map shed in faeces by high shedders, the incubation period duration, and the required infectious dose. Interactions contributed up to 12% of output variance, highlighting the expected added-value of improving several traits simultaneously. Combinations of the four most influential traits decreased incidence to less than one newly infected animal per year in most scenarios. Future genetic selection should aim at improving simultaneously the most influential traits to reduce Map spread in cattle populations

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Additional file 4. Influence of control measure modalities on the probability of non-degrading herd status over a 10-year period. (Panel A) Relative importance of each test-and-cull parameter linked to the predictive statistical model built with the Random Forest Classifier method, and (panel B) associated probability of non-degrading herd status over a 10-year period and according to initial herd status (A2, B, and C, in lines) and reduction of calf exposure (expo = 0.65, 0.5, and 0.35, in columns)

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Additional file 3. Precision of predictive statistical models built with the Random Forest Classifier method. All combinations of initial herd status (early low adult prevalence: A1; low adult prevalence: A2; moderate adult prevalence: B; high adult prevalence: C), year (5, 10, and 15 years), and calf exposure (no reduction: expo = 1; and 3 levels of reduction: expo = 0.65, 0.5, and 0.35) are shown. Accuracies are considered as good enough when above 70%

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Additional file 5. Influence of control measure modalities on the probability of non-degrading herd status over a 15-year period. (Panel A) Relative importance of each test-and-cull parameter linked to the predictive statistical model built with the Random Forest Classifier method, and (panel B) associated probability of non-degrading herd status over a 15-year period and according to initial herd status (A2, B, and C, in lines) and reduction of calf exposure (expo = 0.65, 0.5, and 0.35, in columns)

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Additional file 2. Sample of 11 trajectories of infected adult prevalence obtained with the reference scenario. Reference scenario was defined as the introduction of an infected heifer (IM) in a naĂŻve herd and no control implementation. Each trajectory represents the variation of infected adult prevalence over time since Map introduction. The sample of 11 trajectories has been extracted from the 5000 trajectories used to build initial conditions. Two trajectories (blue and brown) have been highlighted to illustrate model stochasticity and the absence of an early epidemic phase followed by a steady-state prevalence on the contrary to what is classically encountered in epidemiology

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Additional file 1. Model parameters for processes related to population and infection dynamics. Value and source for each parameter of the population and infection dynamics processes