24 research outputs found
Outbreaks of coinfections: the critical role of cooperativity
Modeling epidemic dynamics plays an important role in studying how diseases
spread, predicting their future course, and designing strategies to control
them. In this letter, we introduce a model of SIR
(susceptible-infected-removed) type which explicitly incorporates the effect of
{\it cooperative coinfection}. More precisely, each individual can get infected
by two different diseases, and an individual already infected with one disease
has an increased probability to get infected by the other. Depending on the
amount of this increase, we observe different threshold scenarios. Apart from
the standard continuous phase transition for single disease outbreaks, we
observe continuous transitions where both diseases must coexist, but also
discontinuous transitions are observed, where a finite fraction of the
population is already affected by both diseases at the threshold. All our
results are obtained in a mean field model using rate equations, but we argue
that they should hold also in more general frameworks.Comment: 5 pages, including 5 figure
Impact of individual nodes in Boolean network dynamics
Boolean networks serve as discrete models of regulation and signaling in
biological cells. Identifying the key controllers of such processes is
important for understanding the dynamical systems and planning further
analysis. Here we quantify the dynamical impact of a node as the probability of
damage spreading after switching the node's state. We find that the leading
eigenvector of the adjacency matrix is a good predictor of dynamical impact in
the case of long-term spreading. This so-called eigenvector centrality is also
a good proxy measure of the influence a node's initial state has on the
attractor the system eventually arrives at. Quality of prediction is further
improved when eigenvector centrality is based on the weighted matrix of
activities rather than the unweighted adjacency matrix. Simulations are
performed with ensembles of random Boolean networks and a Boolean model of
signaling in fibroblasts. The findings are supported by analytic arguments from
a linear approximation of damage spreading.Comment: 6 pages, 3 figures, 3 table
Particle velocity controls phase transitions in contagion dynamics
Interactions often require the proximity between particles. The movement of
particles, thus, drives the change of the neighbors which are located in their
proximity, leading to a sequence of interactions. In pathogenic contagion,
infections occur through proximal interactions, but at the same time the
movement facilitates the co-location of different strains. We analyze how the
particle velocity impacts on the phase transitions on the contagion process of
both a single infection and two cooperative infections. First, we identify an
optimal velocity (close to half of the interaction range normalized by the
recovery time) associated with the largest epidemic threshold, such that
decreasing the velocity below the optimal value leads to larger outbreaks.
Second, in the cooperative case, the system displays a continuous transition
for low velocities, which becomes discontinuous for velocities of the order of
three times the optimal velocity. Finally, we describe these characteristic
regimes and explain the mechanisms driving the dynamics.Comment: 9 pages, 5 figures, 12 supplementary figure
Extracting information from S-curves of language change
It is well accepted that adoption of innovations are described by S-curves
(slow start, accelerating period, and slow end). In this paper, we analyze how
much information on the dynamics of innovation spreading can be obtained from a
quantitative description of S-curves. We focus on the adoption of linguistic
innovations for which detailed databases of written texts from the last 200
years allow for an unprecedented statistical precision. Combining data analysis
with simulations of simple models (e.g., the Bass dynamics on complex networks)
we identify signatures of endogenous and exogenous factors in the S-curves of
adoption. We propose a measure to quantify the strength of these factors and
three different methods to estimate it from S-curves. We obtain cases in which
the exogenous factors are dominant (in the adoption of German orthographic
reforms and of one irregular verb) and cases in which endogenous factors are
dominant (in the adoption of conventions for romanization of Russian names and
in the regularization of most studied verbs). These results show that the shape
of S-curve is not universal and contains information on the adoption mechanism.
(published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI:
http://dx.doi.org/10.1098/rsif.2014.1044)Comment: 9 pages, 5 figures, Supplementary Material is available at
http://dx.doi.org/10.6084/m9.figshare.122178
Risk of Coinfection Outbreaks in Temporal Networks: A Case Study of a Hospital Contact Network
We study the spreading of cooperative infections in an empirical temporal network of contacts between people, including health care workers and patients, in a hospital. The system exhibits a phase transition leading to one or several endemic branches, depending on the connectivity pattern and the temporal correlations. There are two endemic branches in the original setting and the non-cooperative case. However, the cooperative interaction between infections reinforces the upper branch, leading to a smaller epidemic threshold and a higher probability for having a big outbreak. We show the microscopic mechanisms leading to these differences, characterize three different risks, and use the influenza features as an example for this dynamics.DFG, 345463468, Interacting Dynamics on Networks, Applications to Epidemiology (idonate
Interplay between competitive and cooperative interactions in a three-player pathogen system
In ecological systems, heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time, multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of human immunodeficiency virus with antibiotic-resistant and susceptible strains of tuberculosis or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We studied this problem considering two cooperating pathogens, where one pathogen is further structured in two strains. The spreading follows a susceptible-infected-susceptible process and the strains differ in transmissibility and extent of cooperation with the other pathogen. We combined a mean-field stability analysis with stochastic simulations on networks considering both well-mixed and structured populations. We observed the emergence of a complex phase diagram, where the conditions for the less transmissible, but more cooperative strain to dominate are non-trivial, e.g. non-monotonic boundaries and bistability. Coupled with community structure, the presence of the cooperative pathogen enables the coexistence between strains by breaking the spatial symmetry and dynamically creating different ecological niches. These results shed light on ecological mechanisms that may impact the epidemiology of diseases of public health concern