9,077 research outputs found
A class of pairwise models for epidemic dynamics on weighted networks
In this paper, we study the (susceptible-infected-susceptible) and
(susceptible-infected-removed) epidemic models on undirected, weighted
networks by deriving pairwise-type approximate models coupled with
individual-based network simulation. Two different types of
theoretical/synthetic weighted network models are considered. Both models start
from non-weighted networks with fixed topology followed by the allocation of
link weights in either (i) random or (ii) fixed/deterministic way. The pairwise
models are formulated for a general discrete distribution of weights, and these
models are then used in conjunction with network simulation to evaluate the
impact of different weight distributions on epidemic threshold and dynamics in
general. For the dynamics, the basic reproductive ratio is
computed, and we show that (i) for both network models is maximised if
all weights are equal, and (ii) when the two models are equally matched, the
networks with a random weight distribution give rise to a higher value.
The models are also used to explore the agreement between the pairwise and
simulation models for different parameter combinations
Complex Agent Networks explaining the HIV epidemic among homosexual men in Amsterdam
Simulating the evolution of the Human Immunodeficiency Virus (HIV) epidemic
requires a detailed description of the population network, especially for small
populations in which individuals can be represented in detail and accuracy. In
this paper, we introduce the concept of a Complex Agent Network(CAN) to model
the HIV epidemics by combining agent-based modelling and complex networks, in
which agents represent individuals that have sexual interactions. The
applicability of CANs is demonstrated by constructing and executing a detailed
HIV epidemic model for men who have sex with men (MSM) in Amsterdam, including
a distinction between steady and casual relationships. We focus on MSM contacts
because they play an important role in HIV epidemics and have been tracked in
Amsterdam for a long time. Our experiments show good correspondence between the
historical data of the Amsterdam cohort and the simulation results.Comment: 21 pages, 4 figures, Mathematics and Computers in Simulation, added
reference
Combination interventions for Hepatitis C and Cirrhosis reduction among people who inject drugs: An agent-based, networked population simulation experiment
Hepatitis C virus (HCV) infection is endemic in people who inject drugs
(PWID), with prevalence estimates above 60 percent for PWID in the United
States. Previous modeling studies suggest that direct acting antiviral (DAA)
treatment can lower overall prevalence in this population, but treatment is
often delayed until the onset of advanced liver disease (fibrosis stage 3 or
later) due to cost. Lower cost interventions featuring syringe access (SA) and
medically assisted treatment (MAT) for addiction are known to be less costly,
but have shown mixed results in lowering HCV rates below current levels. Little
is known about the potential synergistic effects of combining DAA and MAT
treatment, and large-scale tests of combined interventions are rare. While
simulation experiments can reveal likely long-term effects, most prior
simulations have been performed on closed populations of model agents--a
scenario quite different from the open, mobile populations known to most health
agencies. This paper uses data from the Centers for Disease Control's National
HIV Behavioral Surveillance project, IDU round 3, collected in New York City in
2012 by the New York City Department of Health and Mental Hygiene to
parameterize simulations of open populations. Our results show that, in an open
population, SA/MAT by itself has only small effects on HCV prevalence, while
DAA treatment by itself can significantly lower both HCV and HCV-related
advanced liver disease prevalence. More importantly, the simulation experiments
suggest that cost effective synergistic combinations of the two strategies can
dramatically reduce HCV incidence. We conclude that adopting SA/MAT
implementations alongside DAA interventions can play a critical role in
reducing the long-term consequences of ongoing infection
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
Epidemic modelling by ripple-spreading network and genetic algorithm
Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results
Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions
Face-to-face social contacts are potentially important transmission routes
for acute respiratory infections, and understanding the contact network can
improve our ability to predict, contain, and control epidemics. Although
workplaces are important settings for infectious disease transmission, few
studies have collected workplace contact data and estimated workplace contact
networks. We use contact diaries, architectural distance measures, and
institutional structures to estimate social contact networks within a Swiss
research institute. Some contact reports were inconsistent, indicating
reporting errors. We adjust for this with a latent variable model, jointly
estimating the true (unobserved) network of contacts and duration-specific
reporting probabilities. We find that contact probability decreases with
distance, and research group membership, role, and shared projects are strongly
predictive of contact patterns. Estimated reporting probabilities were low only
for 0-5 minute contacts. Adjusting for reporting error changed the estimate of
the duration distribution, but did not change the estimates of covariate
effects and had little effect on epidemic predictions. Our epidemic simulation
study indicates that inclusion of network structure based on architectural and
organizational structure data can improve the accuracy of epidemic forecasting
models.Comment: 36 pages, 4 figure
Equation-Free Multiscale Computational Analysis of Individual-Based Epidemic Dynamics on Networks
The surveillance, analysis and ultimately the efficient long-term prediction
and control of epidemic dynamics appear to be one of the major challenges
nowadays. Detailed atomistic mathematical models play an important role towards
this aim. In this work it is shown how one can exploit the Equation Free
approach and optimization methods such as Simulated Annealing to bridge
detailed individual-based epidemic simulation with coarse-grained,
systems-level, analysis. The methodology provides a systematic approach for
analyzing the parametric behavior of complex/ multi-scale epidemic simulators
much more efficiently than simply simulating forward in time. It is shown how
steady state and (if required) time-dependent computations, stability
computations, as well as continuation and numerical bifurcation analysis can be
performed in a straightforward manner. The approach is illustrated through a
simple individual-based epidemic model deploying on a random regular connected
graph. Using the individual-based microscopic simulator as a black box
coarse-grained timestepper and with the aid of Simulated Annealing I compute
the coarse-grained equilibrium bifurcation diagram and analyze the stability of
the stationary states sidestepping the necessity of obtaining explicit closures
at the macroscopic level under a pairwise representation perspective
Representing the UK's cattle herd as static and dynamic networks
Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using stochastic disease simulations, a wide range of network representations of the UK cattle herd are compared. We find that the simpler static network representations are often deficient when compared with a fully dynamic representation, and should therefore be used only with caution in epidemiological modelling. In particular, due to temporal structures within the dynamic network, static networks consistently fail to capture the predicted epidemic behaviour associated with dynamic networks even when parameterized to match early growth rates
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