BAYESIAN PHYLOGENETICS AND INFECTIOUS DISEASES

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. It is organised by IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano.

The school is organized in cooperation with Fondazione "Alessandro Volta"

The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field.
Each year a different topic is chosen.

Past editions were devoted to: Bayesian Causal Inference, Gene Expression Genomics, Decision Modelling in Health Care, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes, Stochastic Modelling for Systems Biology, Bayesian Methods for Variable Selection with Applications to High-Dimensional Data and Applied Bayesian Nonparametrics, Modern Bayesian Methods and Computing for the Social Sciences, Bayes Big Data and the Internet, Modeling Spatial And Spatio-Temporal Data With Environmental Applications, Bayesian Statistical Modelling and Analysis in Sport, Bayesian Demography.

The lecturer is Prof. Marc Suchard (Department of Biostatistics, UCLA Fielding School of Public Health, USA).
The practical lectures will be given by Filippo Monti (PhD student in Biostatistics, UCLA, USA).

The topic chosen for the 2024 school is
BAYESIAN PHYLOGENETICS AND INFECTIOUS DISEASES.

This course aims to explore the core challenges of Bayesian inference of stochastic processes in modern biology in terms of data-scale, model-dimensionality and compute-complexity. We will illustrate these challenges and learn about emerging statistical solutions through the analysis of biological sequences, such as genes and genomes. Molecular phylogenetics has become an essential analytical tool for understanding the complex patterns in which rapidly evolving pathogens propagate across and between countries, owing to the complex travel and transportation patterns evinced by modern economies, along with growing political factors such as increased global population and urbanisation.
As an accessible course for all, we will start with a brief introduction of the underlying biology (for statistical researchers) and of modern Bayesian inference (for practicing biologists) and then attack our core challenges.

Topics will cover

• probabilistic modeling techniques using both discrete- and continuous-valued stochastic processes including continuous-time Markov chains and Gaussian processes
• large-scale data-integration approaches incorporating factors like human mobility and climate measurements
• recursive computing and other mathematical tricks to evaluate seemingly intractable likelihoods
• state-of-the-art sampling methods for high-dimensional models including Hamiltonian Monte Carlo and its more recent non-reversible extensions
• delivering timely inference on advancing computing hardware like graphics processing units and (maybe even) quantum devices.