Case Study: University of Tokyo improves BSE surveillance with Lumivero @RISK

@RISK Helps Improve Mad Cow Disease Detection in Japan

Professor Katsuaki Sugiura of the University of Tokyo faced the challenge of improving Japan's Bovine Spongiform Encephalopathy (BSE) surveillance program. The long and variable incubation period of the disease, combined with the limited capabilities of diagnostic tests, made it difficult to reliably detect infected cattle that were slaughtered or died before showing symptoms. To address this, he utilized Lumivero's @RISK software.

Using @RISK for Monte Carlo simulation directly within Microsoft Excel, Professor Sugiura built stochastic models to predict how changing the minimum age for BSE testing would impact detection rates. The solution from Lumivero enabled his team to run quantitative risk analyses without needing to learn a new programming language. The results showed that significantly increasing the minimum testing age for slaughtered cattle had very little impact on the probability of detecting infected animals. This insight allowed researchers to eliminate testing age as a critical factor and focus their efforts on more effective measures, thereby improving the overall efficiency of the food safety program.

University of Tokyo

Katsuaki Sugiura

Professor