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meeting ID: 937 6054 7545

password: 946296

Luca Gerardo-Giorda studied Mathematics at the University of Turin, and in 2002 received his Doctorate in Applied Mathematics from the University of Trento. He was awarded a Marie Curie Industry Fellowship at the Institut Francais du Petrole in 2003. After working on applied interdisciplinary research at institutions in Europe (University of Trento, Ecole Polytechnique Paris) and the USA (Emory), in 2014 he set up the group on Mathematical Modeling in Biosciences at BCAM (the Basque Center for Applied Mathematics in Bilbao), that he led until February 2020 when he joined Johannes Kepler University Linz. He is currently the head of the Institute for Mathematical Methods in Medicine and Data-Based Modeling at JKU, and group leader at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) of the Austrian Academy of Sciences. An expert in biomedical modeling and simulation, he seeks quantitative answers to clinical problems, with the aim of providing medical doctors with innovative simulation tools to be efficiently used for in silico pathology assessment and in support of clinical decision making.

Abstract:

In the recent decades, the possibility to simulate complex problems popularised the use of computational models in support for the activity of medical doctors and life scientists. As an example, one of the aims of spatial ecology is to help public health authorities and environmental conservation agencies to take more informed decisions at the time of identifying, monitoring and countering invasive dynamics, be it an infectious disease in wildlife, or the spread of an exogenous species. An accurate computational model can be an efficient predictive tool on which building a proper intervention strategy for the challenge at hand.

In this direction, it is well recognized that quantifying uncertainty is essential for computational predictions to have any real value (as highlighted by the 2014 FDA guidance on use of computational simulation). As an example, the incorrect assumption of perfect knowledge of the model parameters hinders the prediction of relevant Quantities of Interest (QoI) and may result in choosing erroneous interventional strategies. Primary sources of uncertainties may result from input variability (aleatory/irreducible uncertainty), such as the initial conditions, or from a lack of knowledge (epistemic/reducible uncertainty), such as the modeling assumptions or the influence of yet unknown physical or biological phenomena.

Moreover, problems from biomedicine and life science are extremely complex and challenging from the modeling viewpoint. Typically, they are characterised by remarkable heterogeneities and multi-scale dynamics, both in space and time: a reliable predictive mathematical model should be able to soundly cope with these aspects. Unfortunately, more often than not, the available data for model calibration is very limited for a variety of reason, especially in the case of spatial ecology (scarcity of data, limited amounts of economic resources to collect them) or in the presence of a new, poorly known, disease.

In this talk I will present some studies we carried out in the recent years on the spread of invasive species and infectious diseases, where we quantify the uncertainty in the presence of scarce data by combining differential equations (be them ordinary or partial) with Generalised Dynamic Linear Models (in a Bayesian framework) or Polynomial Chaos.