Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour.


Background: The spread of infection amongst livestock depends not only on the traits of the pathogen and the livestock themselves, but also on the behavioural characteristics of farmers and how that impacts the implementation of livestock disease control measures. Livestock owners may change their disease management behaviours in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations and the direct imposition of animal health regulations. Controls that are costly may make it beneficial for individuals to rely on the protection offered by others, though that may be sub-optimal for the population. Thus, failing to account for behavioural dynamics may produce a substantial layer of bias in infectious disease models. Methods: We investigated the role of vaccine behaviour across a population of farmers on epidemic outbreaks amongst livestock, caused by pathogens with differential speed of spread over spatial landscapes of farms for two counties in England (Cumbria and Devon). Under different compositions of three vaccine behaviour groups (precautionary, reactionary, non-vaccination), we evaluated from population- and individual-level perspectives the optimum threshold distance to premises with notified infection that would trigger responsive vaccination by the reactionary vaccination group. Results: On our data-informed livestock systems, we demonstrate a divergence between population and individual perspectives in the optimal scale of reactive voluntary vaccination response. In general, minimising the population-level cost requires a broader reactive uptake of the intervention, with individualistic behaviours increasing the likelihood of larger scale disease outbreaks. When the relative cost of vaccination was low and the majority of premises had undergone precautionary vaccination, then an individual perspective gave a broader spatial extent of reactive response compared to the population perspective. Conclusions: Mathematical models integrating epidemiological and socio-behavioural properties, and the feedback between them, can identify instances of strong disagreement between the intervention stringency that is best for a sole individual compared to the overall population. These modelling insights can aid our understanding of how stakeholders may react to veterinary health interventions.

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