Spatial spread of the Hantavirus infection.

The spatial propagation of Hantavirus-infected mice is considered a serious threat for public health. We analyze the spatial spread of the infected mice by including diffusion in the stage-dependent model for Hantavirus infection recently proposed by Reinoso and de la Rubia [Phys. Rev.

Stage-dependent model for Hantavirus infection: the effect of the initial infection-free period.

We propose a stage-dependent model with constant delay to study the effect of the initial infection-free period on the spread of Hantavirus infection in rodents. We analyze the model under various extreme weather conditions, in the context of the El Niño-La Niña Southern Oscillation phenomenon, and show how these variations determine the evolution of the system significantly. When the scenario corresponds to El Niño, the system presents a demographic explosion and a delayed outbreak of Hantavirus infection, whereas if the scenario is the opposite there is a rapid decline of the population, but with a possible persistence period that may imply a considerable risk for public health, a fact that is in agreement with available field data.

Patterns of phase-dependent spiral wave attenuation in excitable media.

We show that introducing periodic planar fronts with long excitation duration can lead to spiral attenuation. The attenuation occurs periodically over cycles of several planar fronts, forming a variety of complex spatiotemporal patterns. We find that these attenuation patterns occur only at specific phases of the descending fronts relative to the rotational phase of the spiral.

Patterns of spiral wave attenuation by low-frequency periodic planar fronts.

There is evidence that spiral waves and their breakup underlie mechanisms related to a wide spectrum of phenomena ranging from spatially extended chemical reactions to fatal cardiac arrhythmias [A. T. Winfree, The Geometry of Biological Time (Springer-Verlag, New York, 2001); J.

System-size resonance in a binary attractor neural network.

System size resonance (SSR) is a phenomenon in which the response of a system is optimal for a certain finite size, but poorer as the size goes to zero or infinity. In order to show SSR effects in binary attractor neural networks, we study the response of a network, in the ferromagnetic phase, to an external, time-dependent stimulus. Under the presence of such a stimulus, the network shows SSR, as is demonstrated by the measure of the signal amplification both analytically and by simulation.