A stochastic model to assess the risk of introduction of bovine viral diarrhea virus to beef cow–calf herds

Modeling the Effect of Bovine Viral Diarrhea Virus in Australian Beef Herds

Bovine viral diarrhea virus (BVDV) is an economically important disease in Australian beef farming. The disease typically results in low-level production losses that can be difficult to detect for several years. Simulation modeling can be used to support the decision to control BVDV; however, current BVDV simulation models do not adequately reflect the extensive farming environment of Australian beef production. Therefore, the objective of this study was to develop a disease simulation model to explore the impact of BVDV on beef cattle production in south-east Australia. A dynamic, individual-based, stochastic, discrete-time simulation model was created to simulate within-herd transmission of BVDV in a seasonal, self-replacing beef herd. We used the model to simulate the effect of herd size and BVDV introduction time on disease transmission and assessed the short- and long-term impact of BVDV on production outputs that influence the economic performance of beef farms. We found that BVDV can become established in a herd after a single PI introduction in 60% of cases, most frequently associated with the breeding period. The initial impact of BVDV will be more severe in smaller herds, although self-elimination is more likely in small herds than in larger herds, in which there is a 23% chance that the virus can persist for >15 years following a single incursion in a herd with 800 breeders. The number and weight of steers sold was reduced in the presence of BVDV and the results demonstrated that repeat incursions exacerbate long-term production losses, even when annual losses appear marginal. This model reflects the short- and long-term production losses attributed to BVDV in beef herds in southeast Australia and provides a foundation from which the influence and economic utility of BVDV prevention in Australian beef herds can be assessed.

Deterministic and stochastic effects in spreading dynamics: A case study of bovine viral diarrhea

Bovine viral diarrhea (BVD) is a disease in cattle with complex transmission dynamics that causes substantial economic losses and affects animal welfare. The infection can be transient or persistent. The mostly asymptomatic persistently infected hosts are the main source for transmission of the virus. This characteristic makes it difficult to control the spreading of BVD. We develop a deterministic compartmental model for the spreading dynamics of BVD within a herd and derive the basic reproduction number. This epidemiological quantity indicates that identification and removal of persistently infected animals is a successful control strategy if the transmission rate of transiently infected animals is small. Removing persistently infected animals from the herd at birth results in recurrent outbreaks with decreasing peak prevalence. We propose a stochastic version of the compartmental model that includes stochasticity in the transmission parameters. This stochasticity leads to sustained oscillations in cases where the deterministic model predicts oscillations with decreasing amplitude. The results provide useful information for the design of control strategies.

Farm productive contexts and the dynamics of bovine viral diarrhea (BVD) transmission

Bovine viral diarrhea (BVD) is a viral disease that affects cattle and that is endemic to many European countries. It has a markedly negative impact on the economy, through reduced milk production, abortions, and a shorter lifespan of the infected animals. Cows becoming infected during gestation may give birth to Persistently Infected (PI) calves, which remain highly infective throughout their life, due to the lack of immune response to the virus. As a result, they are the key driver of the persistence of the disease both at herd scale, and at the national level. In the latter case, the trade-driven movements of PIs, or gestating cows carrying PIs, are responsible for the spatial dispersion of BVD. Past modeling approaches to BVD transmission have either focused on within-herd or between-herd transmission. A comprehensive portrayal, however, targeting both the generation of PIs within a herd, and their displacement throughout the country due to trade transactions, is still missing. We overcome this by designing a multiscale metapopulation model of the spatial transmission of BVD, accounting for both within-herd infection dynamics, and its spatial dispersion. We focus on Italy, a country where BVD is endemic and seroprevalence is very high. By integrating simple within-herd dynamics of PI generation, and the highly-resolved cattle movement dataset available, our model requires minimal arbitrary assumptions on its parameterization. We use our model to study the role of the different productive contexts of the Italian market, and test possible intervention strategies aimed at prevalence reduction. We find that dairy farms are the main drivers of BVD persistence in Italy, and any control strategy targeting these farms would lead to significantly higher prevalence reduction, with respect to targeting other production compartments. Our multiscale metapopulation model is a simple yet effective tool for studying BVD dispersion and persistence at country level, and is a good instrument for testing targeted strategies aimed at the containment or elimination of this disease. Furthermore, it can readily be applied to any national market for which cattle movement data is available.

Economic risk analysis model for bovine viral diarrhea virus biosecurity in cow-calf herds

A stochastic model was designed to calculate the cost-effectiveness of biosecurity strategies for bovine viral diarrhea virus (BVDV) in cow-calf herds. Possible sources of BVDV introduction considered were imported animals, including the calves of pregnant imports, and fenceline contact with infected herds, including stocker cattle raised in adjacent pastures. Spread of BVDV through the herd was modeled with a stochastic SIR model. Financial consequences of BVDV, including lost income, treatment costs, and the cost of biosecurity strategies, were calculated for 10 years, based on the risks of a herd with a user-defined import profile. Results indicate that importing pregnant animals and stockers increased the financial risk of BVDV. Strategic testing in combination with vaccination most decreased the risk of high-cost outbreaks in most herds. The choice of a biosecurity strategy was specific to the risks of a particular herd.

A Simulation Model to Quantify the Value of Implementing Whole-Herd Bovine Viral Diarrhea Virus Testing Strategies in Beef Cow–Calf Herds

Although numerous diagnostic tests are available to identify cattle persistently infected (PI) with Bovine viral diarrhea virus (BVDV) in cow–calf herds, data are sparse when evaluating the economic viability of individual tests or diagnostic strategies. Multiple factors influence BVDV testing in determining if testing should be performed and which strategy to use. A stochastic model was constructed to estimate the value of implementing various whole-herd BVDV cow–calf testing protocols. Three common BVDV tests (immunohistochemistry, antigen-capture enzyme-linked immunosorbent assay, and polymerase chain reaction) performed on skin tissue were evaluated as single- or two-test strategies. The estimated testing value was calculated for each strategy at 3 herd sizes that reflect typical farm sizes in the United States (50, 100, and 500 cows) and 3 probabilities of BVDV-positive herd status (0.077, 0.19, 0.47) based upon the literature. The economic value of testing was the difference in estimated gross revenue between simulated cow–calf herds that either did or did not apply the specific testing strategy. Beneficial economic outcomes were more frequently observed when the probability of a herd being BVDV positive was 0.47. Although the relative value ranking of many testing strategies varied by each scenario, the two-test strategy composed of immunohistochemistry had the highest estimated value in all but one herd size–herd prevalence permutation. These data indicate that the estimated value of applying BVDV whole-herd testing strategies is influenced by the selected strategy, herd size, and the probability of herd BVDV-positive status; therefore, these factors should be considered when designing optimum testing strategies for cow–calf herds.