The analysis of complex surveillance systems using stochastic scenario tree modelling: Methodology Notes

In the modern landscape of international trade, the SPS agreement of the WTO mandates that measures taken to protect animal, plant, or human health must be based on scientific principles and sufficient evidence. This manual introduces a rigorous framework for quantifying “disease freedom” using stochastic scenario tree modelling.

Moving beyond the limitations of expensive structured surveys and subjective qualitative assessments, this guide presents methods to combine diverse sources of surveillance data—both random and non-random—into transparent, quantitative probability estimates. Developed by Angus Cameron, Tony Martin and Mo Salman, the Danish International EpiLab, and the Australian Biosecurity CRC, this methodology supports science-based risk analysis and creates a robust foundation for asserting disease-free status.

A comprehensive suite of Freedom tools are now available online at EpiMundi’s ShinyEpiTools.