Developing better modelling inference tools to inform disease control for bovine Tuberculosis using epidemiological and pathogen genetic information.

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We shall develop new methods, approaches & guidance to inform endemic and epidemic disease scenarios in multi-host systems via integrated analysis of disease dynamics and pathogen sequence data. We shall test and showcase these tools by application to state of the art phylogenetic data from the cattle-badger Tuberculosis (TB) system in GB. There is an explosion in the availability of pathogen sequence data from disease outbreaks. However, standard methods for longitudinal data analysis that account for disease dynamics do not exploit pathogen sequence data, & standard approaches for pathogen sequence data analysis only abstractly account for the dynamics of disease. Recent work including by the project partners has demonstrated the potential of such integrated analysis in the context of a rapidly evolving pathogen in a single host species (e.g. foot and mouth disease virus in cattle). We shall create the next generation of such spatial phylogenetic epidemic tools to enable for the first time their routine use in the analysis of fast or slowly evolving pathogens in multi-host systems. To achieve this we shall:

1. Create dynamic spatial phylodynamic models for multi-host pathogen systems focussing on TB in badgers and cattle.
2. Develop and assess, under model misspecification, novel Bayesian inference frameworks based on: recently developed importance distributions combined with particle filters; explicit likelihood-based data-augmentation Markov chain Monte Carlo and model assessment tools; and Approximate Bayesian Computation summary statistics.
3. Identify typical signatures for a range of data availabilities and scenarios, from emerging epidemic outbreaks to long term endemic disease, and characterise their potential to inform disease control.
4. Apply the developed methods to data on the badger-cattle-TB system to analyse both core endemic areas and those at the leading edge of epidemic spread, and thus provide insight into this important disease problem