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

Knight, Martin; Hutchings, Michael R.; White, Piran; Davidson, Ross S.; Marion, Glenn

doi:10.1016/j.jtbi.2022.111059

Cited by 4 publications

(5 citation statements)

References 49 publications

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“…[ 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 .…”

Section: Resultsmentioning

confidence: 99%

“…In Knight et al . [ 64 ], farmers' adaptive behaviour (i.e. anticipatory response to disease control interventions) influenced the formation of the network itself.…”

Section: Resultsmentioning

confidence: 99%

“…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).…”

Section: Resultsmentioning

confidence: 99%

“…This includes agents' behavioural adaptations as a response to disease [ 49 , 54 ], or as an unintended consequence following regulatory changes or top-down interventions (e.g. [ 64 ]), as has been observed empirically [ 128 – 131 ].…”

Section: Discussionmentioning

confidence: 99%

“…In an additional model layer in Liu et al [60], individual animal contacts during grazing were modelled using random walks. 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).…”

Section: Agent-based Models (N = 8)mentioning

confidence: 99%

See 4 more Smart Citations

Simulating contact networks for livestock disease epidemiology: a systematic review

Leung

Rudge

Fournié

2023

J. R. Soc. Interface.

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Contact structure among livestock populations influences the transmission of infectious agents among them. Models simulating realistic contact networks therefore have important applications for generating insights relevant to livestock diseases. This systematic review identifies and compares such models, their applications, data sources and how their validity was assessed. From 52 publications, 37 models were identified comprising seven model frameworks. These included mathematical models ( n = 8; including generalized random graphs, scale-free, Watts–Strogatz and spatial models), agent-based models ( n = 8), radiation models ( n = 1) (collectively, considered ‘mechanistic’), gravity models ( n = 4), exponential random graph models ( n = 9), other forms of statistical model ( n = 6) (statistical) and random forests ( n = 1) (machine learning). Overall, nearly half of the models were used as inputs for network-based epidemiological models. In all models, edges represented livestock movements, sometimes alongside other forms of contact. Statistical models were often applied to infer factors associated with network formation ( n = 12). Mechanistic models were commonly applied to assess the interaction between network structure and disease dissemination ( n = 6). Mechanistic, statistical and machine learning models were all applied to generate networks given limited data ( n = 13). There was considerable variation in the approaches used for model validation. Finally, we discuss the relative strengths and weaknesses of model frameworks in different use cases.

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Section: Resultsmentioning

confidence: 99%

“…In Knight et al . [ 64 ], farmers' adaptive behaviour (i.e. anticipatory response to disease control interventions) influenced the formation of the network itself.…”

Section: Resultsmentioning

confidence: 99%