OBJECTIVE: To develop a mathematical model to simulate infection dynamics of Mycobacterium bovis in cattle herds in the United States and predict efficacy of the current national control strategy for tuberculosis in cattle. DESIGN: Stochastic simulation model. SAMPLE: Theoretical cattle herds in the United States. PROCEDURES: A model of within-herd M bovis transmission dynamics following introduction of 1 latently infected cow was developed. Frequency- and density-dependent transmission modes and 3 tuberculin test-based culling strategies (no test-based culling, constant [annual] testing with test-based culling, and the current strategy of slaughterhouse detection-based testing and culling) were investigated. Results were evaluated for 3 herd sizes over a 10-year period and validated via simulation of known outbreaks of M bovis infection. RESULTS: On the basis of 1,000 simulations (1,000 herds each) at replacement rates typical for dairy cattle (0.33/y), median time to detection of M bovis infection in medium-sized herds (276 adult cattle) via slaughterhouse surveillance was 27 months after introduction, and 58% of these herds would spontaneously clear the infection prior to that time. Sixty-two percent of medium-sized herds without intervention and 99% of those managed with constant test-based culling were predicted to clear infection < 10 years after introduction. The model predicted observed outbreaks best for frequency-dependent transmission, and probability of clearance was most sensitive to replacement rate. CONCLUSIONS AND CLINICAL RELEVANCE: Although modeling indicated the current national control strategy was sufficient for elimination of M bovis infection from dairy herds after detection, slaughterhouse surveillance was not sufficient to detect M bovis infection in all herds and resulted in subjectively delayed detection, compared with the constant testing method. Further research is required to economically optimize this strategy.
OBJECTIVE: To develop a mathematical model to simulate infection dynamics of Mycobacterium bovis in cattle herds in the United States and predict efficacy of the current national control strategy for tuberculosis in cattle. DESIGN: Stochastic simulation model. SAMPLE: Theoretical cattle herds in the United States. PROCEDURES: A model of within-herd M bovis transmission dynamics following introduction of 1 latently infected cow was developed. Frequency- and density-dependent transmission modes and 3 tuberculin test-based culling strategies (no test-based culling, constant [annual] testing with test-based culling, and the current strategy of slaughterhouse detection-based testing and culling) were investigated. Results were evaluated for 3 herd sizes over a 10-year period and validated via simulation of known outbreaks of M bovis infection. RESULTS: On the basis of 1,000 simulations (1,000 herds each) at replacement rates typical for dairy cattle (0.33/y), median time to detection of M bovis infection in medium-sized herds (276 adult cattle) via slaughterhouse surveillance was 27 months after introduction, and 58% of these herds would spontaneously clear the infection prior to that time. Sixty-two percent of medium-sized herds without intervention and 99% of those managed with constant test-based culling were predicted to clear infection < 10 years after introduction. The model predicted observed outbreaks best for frequency-dependent transmission, and probability of clearance was most sensitive to replacement rate. CONCLUSIONS AND CLINICAL RELEVANCE: Although modeling indicated the current national control strategy was sufficient for elimination of M bovis infection from dairy herds after detection, slaughterhouse surveillance was not sufficient to detect M bovis infection in all herds and resulted in subjectively delayed detection, compared with the constant testing method. Further research is required to economically optimize this strategy.
Authors: Dianna M Wolfe; Olaf Berke; David F Kelton; Paul W White; Simon J More; James O'Keeffe; S Wayne Martin Journal: Prev Vet Med Date: 2010-03-16 Impact factor: 2.670
Authors: Z Lu; R M Mitchell; R L Smith; J S Van Kessel; P P Chapagain; Y H Schukken; Y T Grohn Journal: J Theor Biol Date: 2008-05-16 Impact factor: 2.691
Authors: R de la Rua-Domenech; A T Goodchild; H M Vordermeier; R G Hewinson; K H Christiansen; R S Clifton-Hadley Journal: Res Vet Sci Date: 2006-03-02 Impact factor: 2.534
Authors: Miguel Mellado; Dulce Reséndiz; Angel Mario Martínez; Maria Angeles de Santiago; Francisco Gerardo Véliz; Jose Eduardo García Journal: Trop Anim Health Prod Date: 2015-04-17 Impact factor: 1.559
Authors: Kimberly VanderWaal; Eva A Enns; Catalina Picasso; Julio Alvarez; Andres Perez; Federico Fernandez; Andres Gil; Meggan Craft; Scott Wells Journal: Sci Rep Date: 2017-06-23 Impact factor: 4.379
Authors: Nicolas Cespedes Cardenas; Pilar Pozo; Francisco Paulo Nunes Lopes; José H H Grisi-Filho; Julio Alvarez Journal: Microorganisms Date: 2021-01-22
Authors: K Renuga Devi; L J Lee; Lee Tze Yan; Amin-Nordin Syafinaz; I Rosnah; V K Chin Journal: Int Arch Occup Environ Health Date: 2021-03-16 Impact factor: 3.015