RESUMEN

The effect of spatial correlations on the spread of infectious diseases was investigated using a stochastic susceptible-infective-recovered (SIR) model on complex networks. It was found that in addition to the reduction of the effective transmission rate, through the screening of infectives, spatial correlations have another major effect through the enhancement of stochastic fluctuations, which may become considerably larger than in the homogeneously mixed stochastic model. As a consequence, in finite spatially structured populations significant differences from the solutions of deterministic models are to be expected, since sizes even larger than those found for homogeneously mixed stochastic models are required for the effects of fluctuations to be negligible. Furthermore, time series of the (unforced) model provide patterns of recurrent epidemics with slightly irregular periods and realistic amplitudes, suggesting that stochastic models together with complex networks of contacts may be sufficient to describe the long-term dynamics of some diseases. The spatial effects were analysed quantitatively by modelling measles and pertussis, using a susceptible-exposed-infective-recovered (SEIR) model. Both the period and the spatial coherence of the epidemic peaks of pertussis are well described by the unforced model for realistic values of the parameters.

Asunto(s)

Brotes de Enfermedades , Transmisión de Enfermedad Infecciosa , Modelos Estadísticos , Redes Neurales de la Computación , Susceptibilidad a Enfermedades , Humanos , Sarampión/epidemiología , Modelos Biológicos , Recurrencia , Tos Ferina/epidemiología

RESUMEN

Standing-wave patterns observed in the CO + O2 reaction on Pt(110) are described by a model that explicitly takes into account the coupling between the transport of adsorbed CO and the adsorbate-induced structural transformation of the substrate. We show that synchronization of the surface is achieved through nucleation and growth processes even in the absence of gas-phase coupling.