Information, opinion and pandemic.

The world's population suffers a COVID-19 pandemic. By September 2020 nearly 1 million people had died. These are official numbers. The real cases might be much higher, due to under-reporting in many countries. Different strategies were adopted by national governments. Neglecting what was defined by sanitarian authorities, some politicians, at the beginning of the pandemic, declared that it would be a little flu, without consequences, lighter than seasonal flues. Some politicians propagated medicines with no scientific support. In many countries and regions, people became confused. The population's reactions to these political positions may facilitate or block the virus spread. In this paper, we propose a model connecting the spreading of opinions with the propagation of a pandemic. We discuss how conflicting opinions can diffuse in the pandemic environment and the influence it has on the population's behavior; how it may cause a greater or smaller number of infected individuals.

Dynamic stability in random and scale-free B-lymphocyte networks.

One of the most intriguing features of the immune system is regulation: a limited response when perturbed repeatedly. We propose a minimal network model for immune regulation in a lymphocyte network containing two types of elements: B lymphocytes and ligands that bind to their receptors. Effective interactions between B cells, mediated by other components of the immune system can be excitatory or inhibitory. In our model, B cell clones and ligand species are represented by nodes, and interactions by links. We expect that, as in many complex systems, the connectivity distribution is broad, motivating study of the model on a scale-free network; for comparison we study the same dynamics on a random graph. We characterize the dynamics of the model and its response to perturbations. Our model reproduces several key features of immune system dynamics: regulation (saturation of response), and more rapid response upon repeated perturbation with the same agents. Our results suggest that a scale-free network of interactions contributes to the regulation and dynamics of the immune system.

Scale-free fuse network and its robustness.

The robustness and reliability of scale-free networks are tested as a fuse network. The idea is to examine the robustness of a scale-free network when links are irreversibly removed after failing. Due to inherent characteristics of the fuse network model, the sequence of links removal is deterministic and conditioned to fuse tolerance and connectivity of its ends. It is a different situation from classical robustness analysis of complex networks, when they are usually tested under random fails and deliberate attacks of nodes. The use of this system to study the fracture of elastic material brought some interesting results.

Survival-extinction phase transition in a bit-string population with mutation.

A bit-string model for the evolution of a population of haploid organisms, subject to competition, reproduction with mutation, and selection, is studied, using mean-field theory and Monte Carlo simulations. We show that, depending on environmental flexibility and genetic variability, the model exhibits a phase transition between extinction due to random drift and survival. For weak selection the population attains a neutral regime. The mean-field theory describes the infinite-size limit, while simulations are used to study quasistationary properties.