Modelling uncertainty

Mathematical and statistical models are powerful tools that, when properly used, help us to understand the underlying processes that drive disease transmission.

With suitable caution they can be an important tool to aid in predicting the outcomes of possible disease control options. Questions that models can be used to address include how long would a Foot-and-Mouth Disease (FMD) epidemic last if it was introduced into Scotland, how important are highly active traders in transmitting Bovine Viral Diarrhoea (BVD), and what coverage of vaccination against Bluetongue Virus (BTV) would be effective in preventing a large outbreak?

EPIC scientists have created a resilient computational framework to respond to emerging diseases and/or changes in industry structure. The 'SOHO' framework is a flexible tool that considers transmission of pathogens within and between herds, network models and the impact of different control strategies. The framework also forensically estimates disease transmission characteristics for particular outbreaks.

In small outbreaks uncertainty is high. However, even in these circumstances, EPIC scientists have now made it possible to quantify the spread of disease between farms and improve model choice. This is critical to accurately assess risks associated with future disease incursions. For example, with as few as 6 or 7 premises identified as positive, we can quantify key characteristics of disease spread and predict risks associated with future disease incursion events, including the total number and likely position of infected farms.

EPIC Publications

A fast algorithm for calculating an expected outbreak size on dynamic contagion networks. J Enright, RR Kao

Broadwick: A framework for computational epidemiology. A O'Hare, SJ Lycett, T Doherty, LCM Salvador, RR Kao

A few bad apples: A model of disease influenced agent behaviour in a heterogeneous contact environment. J Enright, RR Kao

Website design by Innovation Digital Limited