39,710 research outputs found
Non-Markov stochastic dynamics of real epidemic process of respiratory infections
The study of social networks and especially of the stochastic dynamics of the
diseases spread in human population has recently attracted considerable
attention in statistical physics. In this work we present a new statistical
method of analyzing the spread of epidemic processes of grippe and acute
respiratory track infections (ARTI) by means of the theory of discrete
non-Markov stochastic processes. We use the results of our last theory (Phys.
Rev. E 65, 046107 (2002)) to study statistical memory effects, long - range
correlation and discreteness in real data series, describing the epidemic
dynamics of human ARTI infections and grippe. We have carried out the
comparative analysis of the data of the two infections (grippe and ARTI) in one
of the industrial districts of Kazan, one of the largest cities of Russia. The
experimental data are analyzed by the power spectra of the initial time
correlation function and the memory functions of junior orders, the phase
portraits of the four first dynamic variables, the three first points of the
statistical non-Markov parameter and the locally averaged kinetic and
relaxation parameters. The received results give an opportunity to provide
strict quantitative description of the regular and stochastic components in
epidemic dynamics of social networks taking into account their time
discreteness and effects of statistical memory. They also allow to reveal the
degree of randomness and predictability of the real epidemic process in the
specific social network.Comment: 18 pages, 8figs, 1 table
Critical behavior in interdependent spatial spreading processes with distinct characteristic time scales
AbstractThe spread of an infectious disease is well approximated by metapopulation networks connected by human mobility flow and upon which an epidemiological model is defined. In order to account for travel restrictions or cancellation we introduce a model with a parameter that explicitly indicates the ratio between the time scales of the intervening processes. We study the critical properties of the epidemic process and its dependence on such a parameter. We find that the critical threshold separating the absorbing state from the active state depends on the scale parameter and exhibits a critical behavior itself: a metacritical point – a critical value in the curve of critical points – reflected in the behavior of the attack rate measured for a wide range of empirical metapopulation systems. Our results have potential policy implications, since they establish a non-trivial critical behavior between temporal scales of reaction (epidemic spread) and diffusion (human mobility) processes
On the use of human mobility proxy for the modeling of epidemics
Human mobility is a key component of large-scale spatial-transmission models
of infectious diseases. Correctly modeling and quantifying human mobility is
critical for improving epidemic control policies, but may be hindered by
incomplete data in some regions of the world. Here we explore the opportunity
of using proxy data or models for individual mobility to describe commuting
movements and predict the diffusion of infectious disease. We consider three
European countries and the corresponding commuting networks at different
resolution scales obtained from official census surveys, from proxy data for
human mobility extracted from mobile phone call records, and from the radiation
model calibrated with census data. Metapopulation models defined on the three
countries and integrating the different mobility layers are compared in terms
of epidemic observables. We show that commuting networks from mobile phone data
well capture the empirical commuting patterns, accounting for more than 87% of
the total fluxes. The distributions of commuting fluxes per link from both
sources of data - mobile phones and census - are similar and highly correlated,
however a systematic overestimation of commuting traffic in the mobile phone
data is observed. This leads to epidemics that spread faster than on census
commuting networks, however preserving the order of infection of newly infected
locations. Match in the epidemic invasion pattern is sensitive to initial
conditions: the radiation model shows higher accuracy with respect to mobile
phone data when the seed is central in the network, while the mobile phone
proxy performs better for epidemics seeded in peripheral locations. Results
suggest that different proxies can be used to approximate commuting patterns
across different resolution scales in spatial epidemic simulations, in light of
the desired accuracy in the epidemic outcome under study.Comment: Accepted fro publication in PLOS Computational Biology. Abstract
shortened to fit Arxiv limits. 35 pages, 6 figure
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