BioSS has made significant contributions to development of inference methods and their application to a range of important societal challenges
Models + inference make sense of data
- Real world data typically provides a partial and unreliable picture
- Inference for dynamic processes enables estimation - from real world data - of critical features of systems not amenable to direct measurement.
INFERENCE TOOLS
We develop novel - and apply existing -tools to tackle these inference challenges:
- computational speed – we dev. efficient exact & approx. methods
- integrating genetic data with more standard observations
- accounting for heterogeneity in space and time & between individuals
- application to large-scale problems and models of social-systems
APPLICATIONS
- Infectious disease in livestock & wildlife e.g. bovine TB, African Swine Fever, Avian influenza
- Spread of invasive species – UK invasive vascular plants
- Forest pests – Emerald Ash Borer, Great Spruce bark beetle
- COVID-19
- Environmental transmission
- Host genetic effects in disease
- Population dynamics
- Behaviour in social systems
Stochastic process models
- Ideal where chance events play a critical role e.g. disease transmission
- Widely used to model chemical, ecological and epidemiological populations and social systems
- Require parameters – in example shown describing time duration of disease states and infectivity
- Use data to estimate parameters and missing events – in the example shown only see individuals that test positive but not when they become infected
- Also infer model structure – e.g. transmission routes
Example: disease dynamics
- Observations limited by diagnostic tests for disease
