3,691 research outputs found
Modeling the dynamical interaction between epidemics on overlay networks
Epidemics seldom occur as isolated phenomena. Typically, two or more viral
agents spread within the same host population and may interact dynamically with
each other. We present a general model where two viral agents interact via an
immunity mechanism as they propagate simultaneously on two networks connecting
the same set of nodes. Exploiting a correspondence between the propagation
dynamics and a dynamical process performing progressive network generation, we
develop an analytic approach that accurately captures the dynamical interaction
between epidemics on overlay networks. The formalism allows for overlay
networks with arbitrary joint degree distribution and overlap. To illustrate
the versatility of our approach, we consider a hypothetical delayed
intervention scenario in which an immunizing agent is disseminated in a host
population to hinder the propagation of an undesirable agent (e.g. the spread
of preventive information in the context of an emerging infectious disease).Comment: Accepted for publication in Phys. Rev. E. 15 pages, 7 figure
Coupled effects of local movement and global interaction on contagion
By incorporating segregated spatial domain and individual-based linkage into
the SIS (susceptible-infected-susceptible) model, we investigate the coupled
effects of random walk and intragroup interaction on contagion. Compared with
the situation where only local movement or individual-based linkage exists, the
coexistence of them leads to a wider spread of infectious disease. The roles of
narrowing segregated spatial domain and reducing mobility in epidemic control
are checked, these two measures are found to be conducive to curbing the spread
of infectious disease. Considering heterogeneous time scales between local
movement and global interaction, a log-log relation between the change in the
number of infected individuals and the timescale is found. A theoretical
analysis indicates that the evolutionary dynamics in the present model is
related to the encounter probability and the encounter time. A functional
relation between the epidemic threshold and the ratio of shortcuts, and a
functional relation between the encounter time and the timescale are
found
Epidemics in Networks of Spatially Correlated Three-dimensional Root Branching Structures
Using digitized images of the three-dimensional, branching structures for
root systems of bean seedlings, together with analytical and numerical methods
that map a common 'SIR' epidemiological model onto the bond percolation
problem, we show how the spatially-correlated branching structures of plant
roots affect transmission efficiencies, and hence the invasion criterion, for a
soil-borne pathogen as it spreads through ensembles of morphologically complex
hosts. We conclude that the inherent heterogeneities in transmissibilities
arising from correlations in the degrees of overlap between neighbouring
plants, render a population of root systems less susceptible to epidemic
invasion than a corresponding homogeneous system. Several components of
morphological complexity are analysed that contribute to disorder and
heterogeneities in transmissibility of infection. Anisotropy in root shape is
shown to increase resilience to epidemic invasion, while increasing the degree
of branching enhances the spread of epidemics in the population of roots. Some
extension of the methods for other epidemiological systems are discussed.Comment: 21 pages, 8 figure
Understanding HIV/AIDS in the African Context
This book of readings is intended for courses in Global Health. The editors asked Prof. Stillwaggon to contribute a chapter summarizing her years of work on the spread of HIV/AIDS in populations among whom bacterial, fungal, parasitic, and viral diseases are extremely common, particularly in sub-Saharan Africa. Her work has demonstrated that differences in behavior cannot explain differences in HIV rates between world regions
Epidemic model with isolation in multilayer networks
The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the isolation of infected individuals is disregarded. Hence we focus our study in an epidemic model in a two-layer network and we use an isolation parameter w to measure the effect of quarantining infected individuals from both layers during an isolation period tw. We call this process the Susceptible-Infected-Isolated-Recovered (SIIR) model. Using the framework of link percolation we find that isolation increases the critical epidemic threshold of the disease because the time in which infection can spread is reduced. In this scenario we find that this threshold increases with w and tw. When the isolation period is maximum there is a critical threshold for w above which the disease never becomes an epidemic. We simulate the process and find an excellent agreement with the theoretical results.We thank the NSF (grants CMMI 1125290 and CHE-1213217) and the Keck Foundation for financial support. LGAZ and LAB wish to thank to UNMdP and FONCyT (Pict 0429/2013) for financial support. (CMMI 1125290 - NSF; CHE-1213217 - NSF; Keck Foundation; UNMdP; Pict 0429/2013 - FONCyT)Published versio
Epidemic Model with Isolation in Multilayer Networks
The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the
propagation of such airborne diseases as influenza A (H1N1). Although the SIR
model has recently been studied in a multilayer networks configuration, in
almost all the research the isolation of infected individuals is disregarded.
Hence we focus our study in an epidemic model in a two-layer network, and we
use an isolation parameter to measure the effect of isolating infected
individuals from both layers during an isolation period. We call this process
the Susceptible-Infected-Isolated-Recovered () model. The isolation
reduces the transmission of the disease because the time in which infection can
spread is reduced. In this scenario we find that the epidemic threshold
increases with the isolation period and the isolation parameter. When the
isolation period is maximum there is a threshold for the isolation parameter
above which the disease never becomes an epidemic. We also find that epidemic
models, like overestimate the theoretical risk of infection. Finally, our
model may provide a foundation for future research to study the temporal
evolution of the disease calibrating our model with real data.Comment: 18 pages, 5 figures.Accepted in Scientific Report
Characterising two-pathogen competition in spatially structured environments
Different pathogens spreading in the same host population often generate
complex co-circulation dynamics because of the many possible interactions
between the pathogens and the host immune system, the host life cycle, and the
space structure of the population. Here we focus on the competition between two
acute infections and we address the role of host mobility and cross-immunity in
shaping possible dominance/co-dominance regimes. Host mobility is modelled as a
network of traveling flows connecting nodes of a metapopulation, and the
two-pathogen dynamics is simulated with a stochastic mechanistic approach.
Results depict a complex scenario where, according to the relation among the
epidemiological parameters of the two pathogens, mobility can either be
non-influential for the competition dynamics or play a critical role in
selecting the dominant pathogen. The characterisation of the parameter space
can be explained in terms of the trade-off between pathogen's spreading
velocity and its ability to diffuse in a sparse environment. Variations in the
cross-immunity level induce a transition between presence and absence of
competition. The present study disentangles the role of the relevant biological
and ecological factors in the competition dynamics, and provides relevant
insights into the spatial ecology of infectious diseases.Comment: 30 pages, 6 figures, 1 table. Final version accepted for publication
in Scientific Report
Asymmetrically interacting spreading dynamics on complex layered networks
The spread of disease through a physical-contact network and the spread of
information about the disease on a communication network are two intimately
related dynamical processes. We investigate the asymmetrical interplay between
the two types of spreading dynamics, each occurring on its own layer, by
focusing on the two fundamental quantities underlying any spreading process:
epidemic threshold and the final infection ratio. We find that an epidemic
outbreak on the contact layer can induce an outbreak on the communication
layer, and information spreading can effectively raise the epidemic threshold.
When structural correlation exists between the two layers, the information
threshold remains unchanged but the epidemic threshold can be enhanced, making
the contact layer more resilient to epidemic outbreak. We develop a physical
theory to understand the intricate interplay between the two types of spreading
dynamics.Comment: 29 pages, 14 figure
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