Computational Epidemiology and Data Analysis Unit
Description of Research Activity
The research activity of the Computational Epidemiology and Data Analysis Unit involves the use of computational, mathematical, and statistical approaches to model, simulate, visualize and, in general, understand phenomena in veterinary sciences, ranging from the analysis of genomic data both for animal and pathogen species, to the use of geographic information systems in ecology and epidemiology, and to the application of classical mathematical models combined with network science approaches to study epidemiological dynamics.
The Unit intends to move beyond the traditional paradigm of ecological and epidemiological studies through innovative multi-disciplinary research. Our goal is to show how emerging methods and technologies can identify key agents and mechanisms of pathogen dynamics. In particular, the Unit interests and skills include network science modeling, evolutionary computation and machine learning techniques in biodata mining, Bayesian approaches for phylogenetic inference, and geographic information systems application. Overall, our research projects intend to have translational impact on the control and prevention of disease through better epidemiologic understanding of factors influencing disease risk.
Although the principal research line aims to develop network-based approaches to model vector-borne pathogen dynamics, particularly by using bipartite and multipartite graphs, and to study both farm and wild animals’ trades and movements, we are particularly interested in integrating them with geographic and molecular data. We believe that the combination of these different approaches will result in a better understand of the phenomena under investigation, providing models and methods that could finally help in the management and prevention of eco-epidemiological phenomena.
Staff of the Unit
|Mario GIACOBINI||Associate Professoremail@example.com|
|Luigi BERTOLOTTI||Associate Professorfirstname.lastname@example.org|