Self-similarity in pandemic spread and fractal containment policies

Alexander F. Siegenfeld, Asier Piñeiro Orioli, Robin Na, Blake Elias, Yaneer Bar-Yam

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers — a generalization of the basic reproduction number R0 — that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than 1, even if the basic reproduction number R0 is greater than 1. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data.

Read the full article at: arxiv.org

The Third Law of Evolution and The Future of Life: A systems approach to natural philosophy, by Gerard Jagers op Akkerhuis

* Offers an integrating framework for natural philosophy
* Connects biological and physical evolution through novel theory, elaborating an extended evolutionary synthesis
* Analyses science from a philosophical perspective and looks at philosophy from a scientific perspective

More at: link.springer.com

School on Biological Physics and Biomolecular Simulations in the Machine Learning Era

The re-emergence of Machine Learning (ML) in the last decade has started to revolutionize the way we think about science, technology, and even our everyday lives. ML has rapidly become a significant part of research across all scientific areas, including the physical sciences. This school attempts to capture the recent excitement about ML in general and for biophysical and biomolecular systems in particular, addressing participants with various backgrounds ranging from biology or biotechnology to physics.

The school’s purpose is threefold: a) to provide a theoretical foundation from the physicists’ perspective, b) to cross-pollinate different theoretical, experimental, and computational approaches, and c) to develop an overarching perspective that would tie together the various phenomena from biomolecular simulation and electrostatic interactions on the molecular scale to collective behavior of macroscopic biological entities in a unified approach within an ML framework.

There is no registration fee and limited funds are available for travel and local expenses.

This school will be preceded by the II Brazilian Workshop on Soft Matter from April 7-11.

More at: www.ictp-saifr.org

Network community detection via neural embeddings

Sadamori Kojaku, Filippo Radicchi, Yong-Yeol Ahn & Santo Fortunato 

Nature Communications volume 15, Article number: 9446 (2024)

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

Read the full article at: www.nature.com