Generative models of network dynamics provide insight into the effects of trade on endemic livestock disease

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 R 0 . We show how these novel modelling tools enable systems approaches to disease control, using R 0 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 minimize disruption to business by maintaining in-flow of animals for each individual farm reduce R 0 , with the largest reductions for diseases that are most challenging to eradicate. Incentivizing 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.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

“…More recently, the Animal Health Law (AHL, Regulation (EU) 2016/429) sets out requirements for its member states regarding the electronic identification and registration of animals and certain animal products as well as rules for notifying and recording their movements within the EU 15 . Broader availability and enhanced quality of livestock movement data in the EU, together with more accessible computing power and analytical software, have encouraged the study of real-world complex networks in veterinary epidemiology, enabling new insights into the complexity of livestock trades and dynamics of infectious animal disease transmission 16,17 . The analysis of livestock movement data based on graph theory, e.g.…”

Simulating contact networks for livestock disease epidemiology: a systematic review

“…[ 62 ]) 2021 mechanistic ABM SA: disease control yes cattle Ireland bovine herpesvirus type 1 livestock holdings livestock movement dynamic emergent 15 (Knight et al . [ 63 , 64 ]) 2021 mechanistic ABM structure and transmission, behavioural response yes cattle Scotland hypothetical slowly spreading livestock holdings livestock movement dynamic LITS 16 (Kim et al . [ 65 ]; Pomeroy et al .…”

“…In Knight et al . [ 63 , 64 ], a dynamic trade network was generated from defined partnership rules—with the rate at which trade partnerships formed and dissolved, dependent on farms’ in- and out-flow of animals (i.e. supply and demand).…”

A mechanistic model captures livestock trading, disease dynamics, and compensatory behaviour in response to control measures

“…Here we show the effect of adaptation in response to changes in ε b on between-herd disease prevalence by comparing with our previous trading model [2]. For this model, farms are not characterised by at time t stock levels, nor do we include a pricing model.…”