19,456 research outputs found
Competing epidemics on complex networks
Human diseases spread over networks of contacts between individuals and a
substantial body of recent research has focused on the dynamics of the
spreading process. Here we examine a model of two competing diseases spreading
over the same network at the same time, where infection with either disease
gives an individual subsequent immunity to both. Using a combination of
analytic and numerical methods, we derive the phase diagram of the system and
estimates of the expected final numbers of individuals infected with each
disease. The system shows an unusual dynamical transition between dominance of
one disease and dominance of the other as a function of their relative rates of
growth. Close to this transition the final outcomes show strong dependence on
stochastic fluctuations in the early stages of growth, dependence that
decreases with increasing network size, but does so sufficiently slowly as
still to be easily visible in systems with millions or billions of individuals.
In most regions of the phase diagram we find that one disease eventually
dominates while the other reaches only a vanishing fraction of the network, but
the system also displays a significant coexistence regime in which both
diseases reach epidemic proportions and infect an extensive fraction of the
network.Comment: 14 pages, 5 figure
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
Dynamics of new strain emergence on a temporal network
Multi-strain competition on networks is observed in many contexts, including
infectious disease ecology, information dissemination or behavioral adaptation
to epidemics. Despite a substantial body of research has been developed
considering static, time-aggregated networks, it remains a challenge to
understand the transmission of concurrent strains when links of the network are
created and destroyed over time. Here we analyze how network dynamics shapes
the outcome of the competition between an initially endemic strain and an
emerging one, when both strains follow a susceptible-infected-susceptible
dynamics, and spread at time scales comparable with the network evolution one.
Using time-resolved data of close-proximity interactions between patients
admitted to a hospital and medical health care workers, we analyze the impact
of temporal patterns and initial conditions on the dominance diagram and
coexistence time. We find that strong variations in activity volume cause the
probability that the emerging strain replaces the endemic one to be highly
sensitive to the time of emergence. The temporal structure of the network
shapes the dominance diagram, with significant variations in the replacement
probability (for a given set of epidemiological parameters) observed from the
empirical network and a randomized version of it. Our work contributes towards
the description of the complex interplay between competing pathogens on
temporal networks.Comment: 9 pages, 4 figure
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Targeted Recovery as an Effective Strategy against Epidemic Spreading
We propose a targeted intervention protocol where recovery is restricted to
individuals that have the least number of infected neighbours. Our recovery
strategy is highly efficient on any kind of network, since epidemic outbreaks
are minimal when compared to the baseline scenario of spontaneous recovery. In
the case of spatially embedded networks, we find that an epidemic stays
strongly spatially confined with a characteristic length scale undergoing a
random walk. We demonstrate numerically and analytically that this dynamics
leads to an epidemic spot with a flat surface structure and a radius that grows
linearly with the spreading rate.Comment: 6 pages, 5 figure
Multi-state epidemic processes on complex networks
Infectious diseases are practically represented by models with multiple
states and complex transition rules corresponding to, for example, birth,
death, infection, recovery, disease progression, and quarantine. In addition,
networks underlying infection events are often much more complex than described
by meanfield equations or regular lattices. In models with simple transition
rules such as the SIS and SIR models, heterogeneous contact rates are known to
decrease epidemic thresholds. We analyze steady states of various multi-state
disease propagation models with heterogeneous contact rates. In many models,
heterogeneity simply decreases epidemic thresholds. However, in models with
competing pathogens and mutation, coexistence of different pathogens for small
infection rates requires network-independent conditions in addition to
heterogeneity in contact rates. Furthermore, models without spontaneous
neighbor-independent state transitions, such as cyclically competing species,
do not show heterogeneity effects.Comment: 7 figures, 1 tabl
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