Modelling control options for a disease with hidden sub-clinical infection: bacterial kidney disease in Scottish aquaculture
Author(s) -
A. G. Murray,
M. Hall,
L. A. Munro,
I. S. Wallace
Publication year - 2011
Publication title -
chan, f., marinova, d. and anderssen, r.s. (eds) modsim2011, 19th international congress on modelling and simulation.
Language(s) - English
Resource type - Conference proceedings
DOI - 10.36334/modsim.2011.b2.murray
Subject(s) - aquaculture , disease , disease control , kidney disease , bacterial disease , intensive care medicine , medicine , computer science , biology , fishery , virology , fish <actinopterygii> , microbiology and biotechnology
Bacterial kidney disease (BKD) is a disease of salmonids that is present in Western Europe, North and South America and Japan. In Scotland BKD occurs in salmon and trout farms where it has remained at a fairly constant prevalence in spite of an official eradication policy (approximately 1% of salmon and 20% of trout farms are under official controls). The control policy has been based on movement controls on infected sites to prevent spread of infection, and fallowing to clear infection. However, the bacterium responsible for BKD, Renibacterium salmoninarum, can form sub-clinical infection. These subclinical infections can be hard to detect because diagnostic methods used have low sensitivity and within farm prevalence is often low. These subclinical infections are likely to have played an important role in undermining the eradication policy. To investigate control options we have developed an SI model of R. salmoninarum where the model population consists of uninfected, susceptible (S) farms and infected farms. These infected farms are subdivided into 3 categories, diseased (D) and subclinical farms which consist of known (K) and unknown infections (U). The known infections K and D are subject to movement controls and so most infection spread is controlled. The unknown U farms are not controlled and therefore spread infection with fish moved from these sites. This model has been given a sensitivity analysis and used to investigate a range of management scenarios. These include: abandoning movement controls, increased surveillance, improved fallowing on all farms, and vaccination. The response to policy changes depends heavily on the level of undetected infection. Generally, salmon respond more strongly than trout to changes in controls, both positively to stricter and negatively to relaxed controls. Optimal control policies are very different for the two sectors; for trout this being the abandonment of controls and for salmon retaining or reinforcing existing controls. A new control policy has been drawn up that aims to achieve this while separating the two industries to prevent cross infection.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom