Knight, Martin, White, Piran Crawfurd Limond orcid.org/0000-0002-7496-5775, Hutchings, Mike et al. (2 more authors) (2021) Generative models of network dynamics provide insight into the effects of trade on endemic livestock disease. Royal Society Open Science. p. 201715. ISSN 2054-5703
Abstract
We develop and apply analytically tractable generative models of livestock movements at national scale. These go beyond current models through mechanistic modelling of heterogeneous trade partnership network dynamics and the trade events that occur on them. Linking resulting animal movements to disease transmission between farms yields analytical expressions for the basic reproduction number R0. We show how these novel modelling tools enable systems approaches to disease control, using R0 to explore impacts of changes in trading practices on between-farm prevalence levels. Using the Scottish cattle trade network as a case study, we show our approach captures critical complexities of real-world trade networks at the national scale for a broad range of endemic diseases. Changes in trading patterns that minimise disruption to business by maintaining in-flow of animals for each individual farm reduce R0, with the largest reductions for diseases that are most challenging to eradicate. Incentivising high-risk farms to adopt such changes exploits `scale-free' properties of the system and is likely to be particularly effective in reducing national livestock disease burden and incursion risk. Encouragingly, gains made by such targeted modification of trade practices scale much more favourably than comparably targeted improvements to more commonly adopted farm-level biosecurity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Environment and Geography (York) |
Depositing User: | Pure (York) |
Date Deposited: | 10 Feb 2021 10:40 |
Last Modified: | 19 Oct 2024 00:06 |
Published Version: | https://doi.org/10.1098/rsos.201715 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1098/rsos.201715 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170977 |