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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:571972 |
| Date | January 2011 |
| Creators | Delgado, Joao Pedro Correa |
| Contributors | Longhurst, P.; Pollard, Simon |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/7896 |
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