Dr Liliana Salvador
- Research Associate (Institute of Biodiversity Animal Health and Comparative Medicine)
Liliana is a computational ecologist with broad interests in animal movement, infectious disease dynamics, and spatial ecology. She received her Ph.D in Biology from Lisbon University, which was part of the PhD program in Computational Biology at Gulbenkian Institute of Science, under the supervision of Simon A. Levin (Princeton University) and Francisco Dionisio (Lisbon University).
During her PhD, she worked on the ecology and evolution of animal movement focusing on search strategies of microorganisms to understand how animals use information. Using Caenorhabditis elegans as a model system, she studied the mechanistic links between statistical descriptions of movement and behavioural traits, and how these are used to optimize search strategies.
Currently, she is a postdoctoral researcher at the University of Glasgow working with Rowland Kao where she combines empirical data, computer simulations, and theory to better understand the spread of bovine Tuberculosis in a multi-species system based on genomic, movement, and epidemiological information.
Prior to her Ph.D, she studied Computer Science at the University of Porto, where she earned a BSc and a MSc in Theoretical Computer Science focusing on absolute security of cryptosystems using information theory and Kolmogorov Complexity, supervised by Luis Filipe Antunes.
My interests involve studying the interplay between animal movement, infectious disease dynamics, and molecular ecology. Currently, I am working on the comparative analysis of genomic pathogen data using whole genome sequencing to understand the underlying evolutionary processes of bacterial epidemics (in particular bovine Tuberculosis, bTB) at the wildlife/livestock interface.
This involves an integrated approach that combines data analysis, mathematical modeling, and computer simulations in order to reveal the mechanisms of disease transmission and persistence across species at different spatial and temporal scales.
I also have a particular interest in
- understanding the impacts of animal movement patterns on the spatio-temporal disease dynamics
- developing computational tools to deal with large epidemiological, demographic, and animal movement datasets
- making projections for risk-based surveillance in areas with distinct bTB incidence and understanding its impact on the control of the disease.