Epidemiological Monitoring

This field focuses on integrating and analyzing heterogeneous data for the early warning and monitoring of epidemic diseases, using advanced technologies such as machine learning, network analysis, and mathematical modeling.

The spread of pathogens is tracked by integrating environmental sequencing data from urban sewage systems across various European cities, alongside clinical data and human mobility information.

Deep learning algorithms and genomic pipelines are employed to characterize the antigenic properties of rapidly evolving viruses and their response to existing vaccines.

Graph theory, Natural Language Processing (NLP), and Large Language Models (LLMs) are used to analyze textual data from online social networks and scientific literature, aimed at monitoring misinformation, vaccine hesitancy, and emerging trends.

The field also addresses the prediction of health risks related to environmental factors (e.g. heatwaves, seasonal influenza), in close collaboration with local health units.

Collaborations

Computer Laboratory - Cambridge University, Cambridge UK

Centre for Genomic Regulation - CRG, Barcelona ES

Barabasi Lab - Northeastern University, Boston USA

Department of Molecular Biology - Brown University, Providence USA

German Cancer Research Center - DKFZ, Heidelberg DE

Scuola Internazionale Superiore di Studi Avanzati - SISSA, Trieste IT

Istituto di Tecnologie Biomediche - ITB CNR, Milano IT

Istituto Tumori della Romagna - IRST IRCCS, Meldola IT

Policlinico S. Orsola, Bologna IT

Istituti Ortopedici Rizzoli - IOR, Bologna IT

Istituto Nazionale di Fisica Nucleare - INFN, IT