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Complex systems

Our group utilizes renormalization group approaches, machine learning, and information theory to investigate complex systems, including epidemiology, population dynamics, and viral genome evolution. Our applications include developing early warning systems to combat the emergence of new infectious diseases.

Relevant literature:

[1] Renormalization Group Approach to Pandemics: The COVID-19 Case, Della Morte Michele, Orlando Domenico, Sannino Francesco, Frontiers in Physics, volume 8, 2020, 144.

[2] Renormalisation Group approach to pandemics as a time-dependent SIR model, Michele Della Morte, Francesco Sannino, Frontiers in Physics, volume 8, 2021, 583

[3] Interplay of social distancing and border restrictions for pandemics via the epidemic renormalisation group framework, Giacomo Cacciapaglia & Francesco Sannino, Scientific Reports, volume 10, Article number: 15828 (2020)

[4] Second wave COVID-19 pandemics in Europe: a temporal playbook, Giacomo Cacciapaglia, Corentin Cot & Francesco Sannino, Scientific Reports, volume 10, Article number: 15514 (2020)

[5] Calling for pan-European commitment for rapid and sustained reduction in SARS- CoV-2 infections, Viola Priesemann, Melanie M Brinkmann, Sandra Ciesek, Sarah Cuschieri, Thomas Czypionka, Giulia Giordan, Francesco Sannino, et al., The Lancet, volume 397, issue 10269, P9293, January 09, 2021.

[6] Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing, Corentin Cot, Giacomo Cacciapaglia & Francesco Sannino, Scientific Reports, volume 11, Article number: 4150 (2021)

[7] Epidemiological theory of virus variants, Giacomo Cacciapaglia, Corentin Cot, Adele de Hoffer,  Stefan Hohenegger, Francesco Sannino and Shahram Vatania, Physica A: Statistical Mechanics and its Applications, volume 596, 2022, 127071, ISSN 0378-4371.

[8] Impact of US vaccination strategy on COVID-19 wave dynamics, Corentin Cot, Giacomo Cacciapaglia, Anna Sigridur Islind, María Óskarsdóttir & Francesco Sannino, Scientific Reports, volume 11, Article number: 10960 (2021).

[9] Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19, Adele de Hoffer, Shahram Vatani, Corentin Cot, Giacomo Cacciapaglia, Maria Luisa Chiusano, Andrea Cimarelli, Francesco Conventi, Antonio Giannini, Stefan Hohenegger, Francesco Sannino. Scientific Reports, 12, Article 9275.

[10] From Higgs to Pandemics, Francesco Sannino, Archimedes, 2021, Journal of the Finnish Academy of Sciences and Letters. https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5427909119424278407

 [11] Review: The field theoretical ABC of epidemic dynamics, Giacomo Cacciapaglia, Corentin Cot, Michele della Morte, Stefan Hohenegger, Francesco Sannino, Shahram Vatani, https://arxiv.org/abs/2101.11399 

[12] Information Theory Unification of Epidemiological and Population Dynamics, Baptiste Filoche, Stefan Hohenegger, Francesco Sannino, https://arxiv.org/abs/2402.16390

[13] Renormalisation Group Methods for Effective Epidemiological Models, Stefan Hohenegger, Francesco Sannino, https://arxiv.org/abs/2402.16409