Livestock epidemics have the potential to give rise to significant economic, welfare and social costs. However, incursions of emerging and re-emerging pathogens may lead to small outbreaks even when there is risk of a large outbreak. We present a framework for spatial risk assessment of disease incursions based on data from small localised historic outbreaks. In particular, we focus on between farm spread of livestock pathogens and illustrate our methods by application to data on the small outbreak of Classical Swine Fever (CSF) that occurred in 2000 in East Anglia, UK. We apply models based on continuous time semi-Markov processes, using data augmentation Markov chain Monte Carlo (MCMC) techniques within a Bayesian framework to infer disease dynamics and detection from incompletely observed outbreaks. The spatial transmission kernel describing pathogen spread between farms and the distribution of times between infection and detection are estimated alongside unobserved exposure times. Our results demonstrate that inference is reliable even for relatively small outbreaks when the data-generating model is known. However, associated risk assessments depend strongly on the form of the fitted transmission kernel, and therefore for real applications methods are needed to select the most appropriate model in light of the data. We explore the use of standard Deviance Information Criteria (DIC) model selection tools and recently introduced latent residual methods of model assessment, to select the functional form of the spatial transmission kernel. These methods are applied to the CSF data, and tested in simulated scenarios which represent field data, but assume the data generation mechanism is known. Analysis of simulated scenarios shows that latent residual methods enable reliable selection of the transmission kernel even for small outbreaks whereas the DIC is less reliable. Moreover, compared with DIC, model choice based on latent residual assessment correlated better with predicted risk.