67,078 research outputs found
On the Spread of Viruses on the Internet
We analyze the contact process on random graphs generated according to the preferential attachment scheme as a model for the spread of viruses in the Internet. We show that any virus with a positive rate of spread from a node to its neighbors has a non-vanishing chance of becoming epidemic. Quantitatively, we discover an interesting dichotomy: for it virus with effective spread rate λ, if the infection starts at a typical vertex, then it develops into an epidemic with probability λ^Θ ((log (1/ λ)/log log (1/ λ))), but on average the epidemic probability is λ^(Θ (1))
Distributed interaction between computer virus and patch: A modeling study
The decentralized patch distribution mechanism holds significant promise as
an alternative to its centralized counterpart. For the purpose of accurately
evaluating the performance of the decentralized patch distribution mechanism
and based on the exact SIPS model that accurately captures the average dynamics
of the interaction between viruses and patches, a new virus-patch interacting
model, which is known as the generic SIPS model, is proposed. This model
subsumes the linear SIPS model. The dynamics of the generic SIPS model is
studied comprehensively. In particular, a set of criteria for the final
extinction or/and long-term survival of viruses or/and patches are presented.
Some conditions for the linear SIPS model to accurately capture the average
dynamics of the virus-patch interaction are empirically found. As a
consequence, the linear SIPS model can be adopted as a standard model for
assessing the performance of the distributed patch distribution mechanism,
provided the proper conditions are satisfied
Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model
This is the peer reviewed version of the following article: “Alarcón-Soto, Y, Langohr K., Fehér, C., García, F., and Gómez, G. (2018) Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox Model.Biometrical journal, December 13th ”which has been published in final form at [doi: 10.1002/bimj.201700291]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.We present a method to fit a mixed effects Cox model with interval-censored data. Our proposal is based on a multiple imputation approach that uses the truncated Weibull distribution to replace the interval-censored data by imputed survival times and then uses established mixed effects Cox methods for right-censored data. Interval-censored data were encountered in a database corresponding to a recompilation of retrospective data from eight analytical treatment interruption (ATI) studies in 158 human immunodeficiency virus (HIV) positive combination antiretroviral treatment (cART) suppressed individuals. The main variable of interest is the time to viral rebound, which is defined as the increase of serum viral load (VL) to detectable levels in a patient with previously undetectable VL, as a consequence of the interruption of cART. Another aspect of interest of the analysis is to consider the fact that the data come from different studies based on different grounds and that we have several assessments on the same patient. In order to handle this extra variability, we frame the problem into a mixed effects Cox model that considers a random intercept per subject as well as correlated random intercept and slope for pre-cART VL per study. Our procedure has been implemented in R using two packages: truncdist and coxme, and can be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect in a regression model. The properties of the parameter estimators obtained with our proposed method are addressed through a simulation study.Peer ReviewedPostprint (author's final draft
From HIV infection to AIDS: A dynamically induced percolation transition?
The origin of the unusual incubation period distribution in the development
of AIDS is largely unresolved. A key factor in understanding the observed
distribution of latency periods, as well as the occurrence of infected
individuals not developing AIDS at all, is the dynamics of the long lasting
struggle between HIV and the immune system. Using a computer simulation, we
study the diversification of viral genomes under mutation and the selective
pressure of the immune system.In common infections vast spreading of viral
genomes usually does not takes place. In the case of an HIV infection this may
occur, as the virus successively weakens the immune system by depletion of CD4+
cells.In a sequence space framework, this leads to a dynamically induced
percolation transition, corresponding to the onset of AIDS. As a result, we
obtain the prolongated shape of the incubation period distribution, as well as
a finite fraction of non-progressors that do not develop AIDS, comparing well
with results from recent clinical research.Comment: 7 pages RevTeX, 4 figure
Alternatives to the Gypsy Moth Eradication Program in Michigan
Responding to questions of what the gypsy moth, Porthetria dispar, would do in Michigan forests, a computer simulation model was constructed. The model consisted of three subunits: a submodel of gypsy moth population dynamics, a submodel of forest growth and a submodel of tree defoliation and mortality. Several different policies were simulated for an 80 year period. The eradication policy now employed in Michigan failed due to survival of small portions of the population. Allowing the gypsy moth to become established in Michigan forests and then responding by spraying when defoliation is visible provided a policy with the least economic and environmental cost
Does the Red Queen reign in the kingdom of digital organisms?
In competition experiments between two RNA viruses of equal or almost equal
fitness, often both strains gain in fitness before one eventually excludes the
other. This observation has been linked to the Red Queen effect, which
describes a situation in which organisms have to constantly adapt just to keep
their status quo. I carried out experiments with digital organisms
(self-replicating computer programs) in order to clarify how the competing
strains' location in fitness space influences the Red-Queen effect. I found
that gains in fitness during competition were prevalent for organisms that were
taken from the base of a fitness peak, but absent or rare for organisms that
were taken from the top of a peak or from a considerable distance away from the
nearest peak. In the latter two cases, either neutral drift and loss of the
fittest mutants or the waiting time to the first beneficial mutation were more
important factors. Moreover, I found that the Red-Queen dynamic in general led
to faster exclusion than the other two mechanisms.Comment: 10 pages, 5 eps figure
Mathematical modeling of tumor therapy with oncolytic viruses: Effects of parametric heterogeneity on cell dynamics
One of the mechanisms that ensure cancer robustness is tumor heterogeneity,
and its effects on tumor cells dynamics have to be taken into account when
studying cancer progression. There is no unifying theoretical framework in
mathematical modeling of carcinogenesis that would account for parametric
heterogeneity. Here we formulate a modeling approach that naturally takes stock
of inherent cancer cell heterogeneity and illustrate it with a model of
interaction between a tumor and an oncolytic virus. We show that several
phenomena that are absent in homogeneous models, such as cancer recurrence,
tumor dormancy, an others, appear in heterogeneous setting. We also demonstrate
that, within the applied modeling framework, to overcome the adverse effect of
tumor cell heterogeneity on cancer progression, a heterogeneous population of
an oncolytic virus must be used. Heterogeneity in parameters of the model, such
as tumor cell susceptibility to virus infection and virus replication rate, can
lead to complex, time-dependent behaviors of the tumor. Thus, irregular,
quasi-chaotic behavior of the tumor-virus system can be caused not only by
random perturbations but also by the heterogeneity of the tumor and the virus.
The modeling approach described here reveals the importance of tumor cell and
virus heterogeneity for the outcome of cancer therapy. It should be
straightforward to apply these techniques to mathematical modeling of other
types of anticancer therapy.Comment: 45 pages, 6 figures; submitted to Biology Direc
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
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