Process & Systems Modelling

Data driven risk assessment for disease incursions

The ability to respond to livestock disease outbreaks, and thus control their impact by targeting premises at greatest risk, is greatly enhanced if disease dynamics can be accurately characterised and quantified. A key factor to consider in the infection process is the spatial spread of disease between farms, which in livestock epidemic models is typically characterised by transmission, or kernel density functions modulated as a function of distance between farms. In situations of re-emerging diseases, for which small but repeated outbreaks often occur, extracting maximal information about disease spread is statistically challenging.

We have created a novel toolkit for scientists and policy-makers to make better use of data from past disease incursions than is possible with current methods. Even when such outbreaks are small, simulated test cases show we can make use of the limited data available to more confidently quantify the spread of disease between farms, including choosing between different transmission functions using novel latent-residual methods devised in collaboration with Heriot-Watt University. Our tools, therefore, provide critical information for risk assessment of future disease incursions.

risk maps of eastern England areaRisk maps of the eastern England area showing two different transmission functions. Scale ranges from pale pink (highest risk) to grey green (lowest risk). Infection risk maps of farm premises during an incursion of classical swine fever in eastern England using two models with different transmission functions for spatial spread. Our latent residual method indicates the spatial transmission function underlying the lefthand map does not fit the small historical data sets as well as the transmission function underlying the right-hand map.

Further details from: Kokouvi Gamado and Glenn Marion

Article date 2015

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Statistical Genomics and Bioinformatics

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