4,719 research outputs found
Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.Comment: Minor changes and updates to reference
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
Community Lynching and the US Asthma Epidemic
We explore the implications of IR Cohen's work on immune cognition for understanding rising rates of asthma morbidity and mortality in the US. Immune cognition is inherently linked with central nervous system cognition, and with the cognitive function of the embedding sociocultural networks by which individuals are acculturated and through which they work with others to meet challenges of threat and opportunity. Externally-imposed patterns of 'structured stress' can, through their effect on a child's socioculture, become synergistic with the development of immune cognition, triggering the persistence of an atopic Th2 phenotype, a necessary precursor to asthma and other immune diseases. Structured stress in the US particularly includes the cross sectional and longitudinal effects of a systematic destruction of minority urban communities occurring since the end of World War II which we characterize as community lynching. Reversal of the rising tide of asthma and related chronic diseases in the US thus seems unlikely without a 21st Century version of the earlier Great Urban Reforms which ended the scourge of infectious diseases, in particular an end to the de-facto ethnic cleansing of minority neighborhoo
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission
Multipartite viruses replicate through a puzzling evolutionary strategy.
Their genome is segmented into two or more parts, and encapsidated in separate
particles that appear to propagate independently. Completing the replication
cycle, however, requires the full genome, so that a systemic infection of a
host requires the concurrent presence of several particles. This represents an
apparent evolutionary drawback of multipartitism, while its advantages remain
unclear. A transition from monopartite to multipartite viral forms has been
described in vitro under conditions of high multiplicity of infection,
suggesting that cooperation between defective mutants is a plausible
evolutionary pathway towards multipartitism. However, it is unknown how the
putative advantages that multipartitism might enjoy at the microscopic level
affect its epidemiology, or if an explicit advantange is needed to explain its
ecological persistence. To disentangle which mechanisms might contribute to the
rise and fixation of multipartitism, we investigate the interaction between
viral spreading dynamics and host population structure. We set up a
compartmental model of the spread of a virus in its different forms and explore
its epidemiology using both analytical and numerical techniques. We uncover
that the impact of host contact structure on spreading dynamics entails a rich
phenomenology of ecological relationships that includes cooperation,
competition, and commensality. We find that multipartitism might rise to
fixation even in the absence of explicit microscopic advantages. Multipartitism
allows the virus to colonize environments that could not be invaded by the
monopartite form, facilitated by homogeneous contacts among hosts. We
conjecture that these features might have led to an increase in the diversity
and prevalence of multipartite viral forms concomitantly with the expansion of
agricultural practices.Comment: 27 pages, 4 figures, 1 tabl
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