Palisade’s risk analysis software @RISK is being used by aquatic veterinary surgeons to demonstrate the practice of biosecurity to aquatic farmers. The method helps to reduce the potential for disease in animals without incurring the significant costs of extensive testing. Only a small number of data inputs are required for @RISK, with thousands of simulations then presenting accurate results that inform decision-making.
It is estimated that the human population will be nine billion by 2030. The Food and Agriculture Organization (FAO) believes that aquaculture, which currently provides around half of the fish and shellfish eaten around the world, is the only agricultural industry with the potential to meet the protein requirements of this population. However, one of the biggest constraints to achieving this is the depletion of stock levels through disease. In 1997, the World Bank estimated that annual losses amounted to $3 billion, and current figures suggest that 40 percent of insured losses are due to disease.
Biosecurity measures, which aim to prevent, control and ideally eradicate disease are regarded as essential. However, encouraging the adoption of these practices is often difficult due to the farmers’ levels of education, training, responsibility and perceived economic benefits. In addition, global estimates of disease losses may appear remote and irrelevant to farmers and producers faced with making a rational choice from scarce data and, often scarcer, resources.
@RISK Makes it Easy to Show Risk of Disease
Dr Chris Walster is a qualified veterinary surgeon with a long-standing interest in aquatic veterinary medicine, and is the secretary to the World Aquatic Veterinary Medical Association (WAVMA). Having seen Palisade’s risk analysis tool, @RISK, demonstrated, he started using it to calculate the realistic risk of aquatic disease to farms, with a focus on cases where data inputs were limited.
@RISK’s capacity to present the calculations in graphs that are easy to understand also makes it straightforward for vets to show farmers disease risk probabilities. With this information readily available, the cost/benefit of disease prevention can be calculated, and farmers can make informed choices about whether to put controls in place.
@RISK Provides Accurate Forecast Without Extensive Testing
For example, a farmer might plan to import 1000 fish to their farm. The cost to accurately determine the disease status of these fish may be uneconomic, but testing a small sample will not give sufficient evidence on which to base an informed purchase decision.
However, testing 30 of the fish and running further simulations using @RISK will give the probability of how many fish might be diseased if more were tested. In other words it provides the farmer with a more accurate picture of the risk connected to purchasing the stock.
If there is no information as to whether the fish carry a disease of interest, testing 30 of them would be expected to return the results that 15 are diseased and 15 are not (a disease prevalence of 0.5 must be assumed, giving a 50/50 probability). However, because tests are rarely 100% accurate, when interpreting a test result, its validity, or how well it performs must also be accounted for. This requires knowing the test characteristics, sensitivity (test positive and truly disease positive) and specificity (test negative and truly disease negative) along with the disease prevalence (or likelihood).
Introducing a sensitivity of 80% for example reduces the fish testing positive to twelve (15 x 0.8). In this case, using a specificity of 98% the simulation is run 10,000 times to produce enough ‘values’, and these are used to produce a graph showing likely minimum, maximum and mean prevalence of the disease.
This simple example helps to generate understanding amongst farmers that they do not need to undertake extensive testing programs to obtain accurate results about disease levels in fish.
Dr. Chris Walster
World Aquatic Veterinary Medical Association
@RISK Incorporates the Human Element
Further evidence can be gathered by running more tests that supplement the @RISK distribution graphs with prior knowledge – facts that are already known and accepted. For example, international regulations make it illegal to transport sick animals. Therefore, if a particular disease shows obvious symptoms, it seems reasonable to assume (using human expertise) that the prevalence of the disease is no higher than 10%, or the seller would have noticed that the fish were sick and could not be sold. Once again, only 30 fish are tested, but this time @RISK is used for a PERT distribution with expert opinion introducing a minimum of 1%, most likely of 5% and maximum of 10%. Running the @RISK simulation 10,000 times again to produce significantly more values can change the results significantly.
@RISK Simulation Informs Decision-Making
With this knowledge, the farmer can now decide on the next course of action. They may decide they are happy with the potential risk and buy the fish. Equally they may want more certainty and therefore test more fish or use additional tests. Finally they may feel that the risk is too great and research other sources.
“@RISK enables farmers to reduce the risk of disease spreading amongst their animals whilst minimizing additional costs,” Walster explains: “For aquatic vets, the key is the graphs which allow us to demonstrate a complex probability problem quickly and simply in a way that is easy to understand and trust. These inform decision-making, thereby helping to boost the world’s aquatic stock whilst safeguarding farmers’ livelihoods.”
“This technique also potentially offers an economical method of assisting in the control of many diseases. Farmers undertake their own tests, with each of these providing incremental inputs so that the macro picture can be developed and acted upon,” concludes Walster.