Developing better modelling inference tools to inform disease control for bovine Tuberculosis using epidemiological and pathogen genetic information.
Lead Research Organisation:
James Hutton Institute
Department Name: BIOSS
Abstract
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Technical Summary
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
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
Description | Bayes Comp 2023., Levi, Finland |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Bayes Comp is the biennial conference of the Bayesian Computation Section of the International Society for Bayesian Analysis, attended by a member of the research team to assess new and emerging methods in Bayesian statistics. |
Year(s) Of Engagement Activity | 2023 |
URL | https://bayescomp2023.com/ |
Description | Meeting with George Freeman MP, UK Minister for Science, Research and Innovation |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | Meet with George Freeman MP, Minister for Science, Research and Innovation - Accompanied by: Rachel Smith, BEIS Head of Business Innovation, Isobel Stephen, UKRI Executive Director. Discussed national and international opportinities for UK science and the role of the agri-biosciences sector in Scotland, including in the areas of sustainable agriculture and biosecuirty. |
Year(s) Of Engagement Activity | 2023 |
Description | Workshop on Bayesian Inference of Epidemics |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Participation by team members in technical workshop sharing and exploring ideas related to advancement of inference techniques for process models especially models of epidemic processes |
Year(s) Of Engagement Activity | 2023 |
URL | https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/corbella/bcepidemics2023 |