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Serious Gaming in Animal Disease Control

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  • Full or part time
    Dr Van Klink
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

Notifiable diseases of animals require specific control measures and are often impossible for the individuals to tackle. Consequently, central governments are generally involved in bringing these diseases under control. A wide variety of diseases fall into this category, in a case such as bovine TB, the behaviour of the causative organism and the factors associated with the spread of the disease mean that any control programme takes a very long time to complete. However, when viral diseases such as foot and mouth break out everything is done to bring them under control as fast as possible. This requires many complicated measures to be implemented within a very short time, and the rarity of such outbreaks means that decision makers generally don’t have experience of previous outbreaks and have to adjust within a very short time to sometimes extremely volatile circumstances.

Serious gaming has been designed to be used for training, education, marketing and design. Video games technology is used to create interactive virtual worlds in which the players have to take decisions that resemble decisions made in real life (Knight et al., 2012). There is potential to use this technology in the veterinary world, and the purpose of this project is to build a serious game for animal disease outbreak control.

The agricultural sector is probably one of the best documented sectors in the economy. Locations of all farms are registered and all major livestock species have to be identified and registered throughout their lives. Direct contact between animals is a major cause of disease transmission, making it imperative that when diseased animals are identified, farms and animals that have been in contact with them and consequently are “at risk” are rapidly located and control measures implemented. Previously the cattle movement scheme has been used to mimic a disease outbreak by “planting” an imaginary disease on a real life farm and follow through the system where this disease might end up (Vernon and Keeling, 2012). The documentation available on so many aspects of the livestock sector could form a good basis a game.

A considerable body of science has been built up in the field of simulation modelling. Many of these models use Markov Chain Monte Carlo simulation, where a series of events leading up to an outcome is modelled in sequence (Mangen et al., 2004). Along the modelled pathway, each event can have several outcomes. The likelihood of any of these outcomes is modelled on the basis of research results related to the respective node in the pathway, or on the basis of assumptions. In addition, Geographical Information Systems are increasingly used in animal disease control (Vose et al., 2012). It is possible to combine these systems and modelling approaches to create spatial and temporal models of disease outbreak development.

The basic systems and information seem to be present to enable creation of a game that mimics animal disease outbreaks. The game would lead the player through the events happening during disease outbreak. The player would have to react to messages about the spread of the disease and take timely measures to prevent further spread and to eradicate the disease as with the passing of time the chances of disease spread further increase.

References

Knight, J.F., S. Carley, B. Tregunna, S. Jarvis, R. Smithies, S. de Freitas, I. Dunwell, K. Mackway-Jones, 2010. Serious gaming technology in major incident triage training: A pragmatic controlled trial. Resuscitation, Volume 81, Issue 9, Pages 1175–1179.
Vernon, M.C., and M.J. Keeling, 2012. Impact of regulatory perturbations to disease spread through cattle movements in Great Britain. Preventive Veterinary Medicine 105 (2012) 110– 117.
Mangen, M.-J.J., A.M. Burrell and M.C.M. Mourits, 2004. Epidemiological and economic modelling of classical swine fever: application to the 1997/1998 Dutch epidemic. Agricultural Systems 81 (2004) 37–54.
Vose, D., N. Alexander, W. Wint, K. Mintiens, E. Ducheyne, G. Hendrickx, K.L. Ebi, 2012. VIMAP: Mapping European Health Vulnerabilities To Climate Change Related To Communicable Disease. Lot 2: Modelling of Vulnerabilities. Draft final Report January, 2012. European Centre for Disease Prevention and Control.

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