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Modelling bluetongue virus transmission between farms using animal and vector movements.

ABSTRACT: Bluetongue is a notifiable disease of ruminants which, in 2007, occurred for the first time in England. We present the first model for bluetongue that explicitly incorporates farm to farm movements of the two main hosts, as well as vector dispersal. The model also includes a seasonal vector to host ratio and dynamic restriction zones that evolve as infection is detected. Batch movements of sheep were included by modelling degree of mixing at markets. We investigate the transmission of bluetongue virus between farms in eastern England (the focus of the outbreak). Results indicate that most parameters affecting outbreak size relate to vectors and that the infection generally cannot be maintained without between-herd vector transmission. Movement restrictions are effective at reducing outbreak size, and a targeted approach would be as effective as a total movement ban. The model framework is flexible and can be adapted to other vector-borne diseases of livestock.

SUBMITTER: Turner J

PROVIDER: S-EPMC3307041 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Modelling bluetongue virus transmission between farms using animal and vector movements.

Turner Joanne J Bowers Roger G RG Baylis Matthew M

Scientific reports 20120319

Bluetongue is a notifiable disease of ruminants which, in 2007, occurred for the first time in England. We present the first model for bluetongue that explicitly incorporates farm to farm movements of the two main hosts, as well as vector dispersal. The model also includes a seasonal vector to host ratio and dynamic restriction zones that evolve as infection is detected. Batch movements of sheep were included by modelling degree of mixing at markets. We investigate the transmission of bluetongue ...[more]

PMID: 22432051

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Project description:The transmission of vector-borne diseases is governed by complex factors including pathogen characteristics, vector-host interactions, and environmental conditions. Temperature is a major driver for many vector-borne diseases including Bluetongue viral (BTV) disease, a midge-borne febrile disease of ruminants, notably livestock, whose etiology ranges from mild or asymptomatic to rapidly fatal, thus threatening animal agriculture and the economy of affected countries. Using modeling tools, we seek to predict where the transmission can occur based on suitable temperatures for BTV. We fit thermal performance curves to temperature-sensitive midge life-history traits, using a Bayesian approach. We incorporate these curves into S(T), a transmission suitability metric derived from the disease's basic reproductive number, [Formula: see text] This suitability metric encompasses all components that are known to be temperature-dependent. We use trait responses for two species of key midge vectors, Culicoides sonorensis and Culicoides variipennis present in North America. Our results show that outbreaks of BTV are more likely between 15[Formula: see text] C and [Formula: see text], with predicted peak transmission risk at 26 [Formula: see text] C. The greatest uncertainty in S(T) is associated with the following: the uncertainty in mortality and fecundity of midges near optimal temperature for transmission; midges' probability of becoming infectious post-infection at the lower edge of the thermal range; and the biting rate together with vector competence at the higher edge of the thermal range. We compare three model formulations and show that incorporating thermal curves into all three leads to similar BTV risk predictions. To demonstrate the utility of this modeling approach, we created global suitability maps indicating the areas at high and long-term risk of BTV transmission, to assess risk and to anticipate potential locations of disease establishment.

Dataset Information

Modelling bluetongue virus transmission between farms using animal and vector movements.

Publications

Modelling bluetongue virus transmission between farms using animal and vector movements.

Similar Datasets

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