Peer-reviewed veterinary case report
Existing and potential use of models in the control and prevention of disease emergencies affecting aquatic animals.
- Journal:
- Revue scientifique et technique (International Office of Epizootics)
- Year:
- 2008
- Authors:
- Murray, A G
- Affiliation:
- Fisheries Research Services Marine Laboratory · United Kingdom
Plain-English summary
This research discusses how models can help manage and prevent disease outbreaks in aquatic animals, like fish. Models are tools that help decision-makers understand and plan for emergencies, especially when there isn't much time or data available. The study highlights four key areas where these models can be useful: understanding what causes disease outbreaks, designing effective monitoring systems, predicting how diseases spread, and assessing the impact of outbreaks. Some types of models, like those that analyze risk and disease spread, have already been used quite a bit, and there's a push to use more models in the future to improve disease management. Overall, the research suggests that using these models can significantly enhance the response to disease emergencies in aquatic animals.
Abstract
Models are tools that aid managers to make decisions in a transparent manner. Models are implicitly used to devise any management plan, but scientific modelling makes the approach explicit and transparent. Simple models are often more useful than complex models, especially when time and data are short--as in many emergency situations. Four areas in which modelling can help aquatic animal health managers to control or prevent disease emergencies are identified, and their application reviewed. These areas are: models of factors behind disease outbreaks; models for the design of efficient surveillance; models of disease spread (subdivided into Susceptible-Infected-Removed [SIR] models, coupled hydrodynamic-particle transport models and network models); and models to evaluate the consequences of disease outbreaks. Import risk analysis and SIR modelling have been applied fairly extensively; risk-based surveillance is likely to be a driver for increased modelling effort in the near future.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/18666489/