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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
Infection Curve Flattening via Targeted Interventions and Self-Isolation
Understanding the impact of network clustering and small-world properties on
epidemic spread can be crucial in developing effective strategies for managing
and controlling infectious diseases. Particularly in this work, we study the
impact of these network features on targeted intervention (e.g., self-isolation
and quarantine). The targeted individuals for self-isolation are based on
centrality measures and node influence metrics. Compared to our previous works
on scale-free networks, small-world networks are considered in this paper.
Small-world networks resemble real-world social and human networks. In this
type of network, most nodes are not directly connected but can be reached
through a few intermediaries (known as the small-worldness property). Real
social networks, such as friendship networks, also exhibit this small-worldness
property, where most people are connected through a relatively small number of
intermediaries. We particularly study the epidemic curve flattening by
centrality-based interventions/isolation over small-world networks. Our results
show that high clustering while having low small-worldness (higher shortest
path characteristics) implies flatter infection curves. In reality, a flatter
infection curve implies that the number of new cases of a disease is spread out
over a longer period of time, rather than a sharp and sudden increase in cases
(a peak in epidemic). In turn, this reduces the strain on healthcare resources
and helps to relieve the healthcare services
Spread of Infectious Diseases with a Latent Period
Infectious diseases spread through human networks.
Susceptible-Infected-Removed (SIR) model is one of the epidemic models to
describe infection dynamics on a complex network connecting individuals. In the
metapopulation SIR model, each node represents a population (group) which has
many individuals. In this paper, we propose a modified metapopulation SIR model
in which a latent period is taken into account. We call it SIIR model. We
divide the infection period into two stages: an infected stage, which is the
same as the previous model, and a seriously ill stage, in which individuals are
infected and cannot move to the other populations. The two infectious stages in
our modified metapopulation SIR model produce a discontinuous final size
distribution. Individuals in the infected stage spread the disease like
individuals in the seriously ill stage and never recover directly, which makes
an effective recovery rate smaller than the given recovery rate.Comment: 6 pages, 3 figure
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