Modelling the impact of reporting rates on outbreak detection with implications for managing emergency animal diseases

Detection and surveillance of emergency animal disease outbreaks play a crucial role in their management. However, monitoring disease spread often relies on farmers self-reporting animal infection. If there are disincentives for farmers to report disease, this delayed surveillance can impact outbreak management outcomes. To understand how farmer reporting rates impact outbreak management strategies, we model the spread of animal disease using an agent-based model. We investigate how varying the reporting rate of infected properties can impact the absolute outcomes of management strategies and which strategy is optimal. Our model considers disease transmission occurring within a property, through animal-to-animal transmission, and between properties, through wind dispersal of fomites and random movement of animals. Using this model, we compare the number of animals culled under four strategies: culling infected properties and animal movement restrictions combined with (1) ring culling, (2) ring testing, (3) ring vaccination (with a perfect vaccine), and (4) ring vaccination (with an imperfect vaccine). Our modelling demonstrates how human behaviour, such as reporting rates, can impact the outcomes from managing emergency animal disease outbreaks. While exact behaviour cannot be predicted for future outbreaks, we can prepare for the next outbreak of emergency animal disease by designing management strategies that are robust to a variety of human behaviours.

Modelling the impact of reporting rates on outbreak detection with implications for managing emergency animal diseases

Monday, July 14 at 6:00pm

Isobel Abell

"Modelling the impact of reporting rates on outbreak detection with implications for managing emergency animal diseases"

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