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10:00-11:00 Session 16: Plenary 3

Kerrie Mengersen, Queensland University of Technology (Australia)

Chairperson: David Conesa, Universitat de Valencia (Spain)

Title: Sloppy models: unveiling parameter uncertainty in mathematical models


In this presentation, I will discuss a Bayesian approach to assessing the sensitivity of model outputs to changes in parameter values in mathematical models, constrained by the combination of prior beliefs and data. The approach identifies stiff parameter combinations that strongly affect the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily from the data or are also substantively influenced by the priors. These stiff parameter combinations can uncover controlling mechanisms underlying the system being modeled and guide future experiments for improved parameter inference. The focus of the discussion will be on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated. The approach will be illustrated with applications in biochemistry, ecology, and cardiac electrophysiology.

This work is joint with Gloria Monsalve-Bravo (lead author), Brodie Lawson, Christopher Drovandi, Kevin Burrage, Kevin Brown, Christopher Baker, Sarah Vollert, Eve McDonald-Madden and Matthew Adams.

The full paper is available as an arXiv preprint arXiv:2203.15184


11:00-11:30Coffee Break
11:30-13:00 Session 17A: Survival analysis

Chairperson: Klaus Langohr, Universitat Politecnica de Catalunya (Spain)

Location: Aula -1.A.06
A solution to implement reference-based imputation with time-to-event endpoints through the illness-death multi-state model
Non-Markov multistate models applied to a cohort of COVID-19 patients
A prediction model for survival in patients with advanced disease: dealing with non-proportional hazards factors.
A semi-Markov multistate model for in-hospital survival to examine the first COVID-19 wave in the Barcelona metropolitan area
A multi-state model to analyze hospitalized Covid-19 patients during the first three waves in the Barcelona metropolitan area
11:30-13:00 Session 17B: Bio-Bayes modelling

Chairperson: María Eugenia Castellanos, Universidad Rey Juan Carlos (Spain)

Location: Aula -1.A.07
A Bayesian approach to population estimation in capture-recapture models
Urban greening and gentrification
Reducing the use of fungicides in agriculture by 50% with decision support systems. A Bayesian meta-analysis
Metamodelling of multimodelling: a Bayesian meta predictive-model for Climate Change
Multivariate analysis of the determinants of quality composts from green waste streams from different origins using Bayesian networks
14:30-15:30 Session 18: Plenary 4

Pere Puig, Universidad Autónoma de Barcelona (Spain)

Chairperson: Carmen Armero, Universitat de Valencia (Spain)

Title: I've been irradiated!! What is the total amount of radiation I've re- ceived?


In the event of a radiation accident, biological dosimetry is critical for determining the radiation dose received by an exposed individual in a timely way. The dose is estimated by calculating the amount of damage caused by radiation at the cellular level, such as by counting the number of chromosome aberrations like dicentrics micronuclei, or translocations. The theory of count data distributions is critical for achieving this goal. In this talk, we will introduce the standard statistical methodology for dose estimation described in the International Atomic Energy Agency's manual (IAEA, 2011), as well as summarise recent research led by our team. We will present models based on compound Poisson processes that are suitable for describing high-LET radiation exposures such as those seen in the Fukushima accident, zero-inated and mixed Poisson models for partial and heterogeneous exposures, and weighted Poisson models for integrating low and high doses.

Location: Auditorio
16:30-17:00Coffee Break