Abstract:
Context and rationale – This work originates from policy priorities established within
Defra to manage exotic animal diseases (EAD); specifically to understand the causes of
low probability events, and to establish contingencies to manage outbreak incidents.
Outbreaks of exotic animal diseases, e.g. FMD, CSF and HPAI, can cause economic
and social impacts of catastrophic proportions. The UK’s government develops and
implements policies and controls to prevent EAD and thus minimise these impacts.
Control policies to achieve this are designed to address the vulnerabilities within the
control systems. However, data are limited for both the introduction of an EAD as well
as its resurgence following the disposal of infected carcasses, i.e. the pre-outbreak and
post-outbreak phases of an EAD event. These lack of data compromises the
development of policy interventions to improve protection. To overcome these data
limitations, predictive models are used to predict system vulnerabilities. Cont/d.
