27,509 research outputs found
Epidemic spreading on preferred degree adaptive networks
We study the standard SIS model of epidemic spreading on networks where
individuals have a fluctuating number of connections around a preferred degree
. Using very simple rules for forming such preferred degree networks,
we find some unusual statistical properties not found in familiar
Erd\H{o}s-R\'{e}nyi or scale free networks. By letting depend on the
fraction of infected individuals, we model the behavioral changes in response
to how the extent of the epidemic is perceived. In our models, the behavioral
adaptations can be either `blind' or `selective' -- depending on whether a node
adapts by cutting or adding links to randomly chosen partners or selectively,
based on the state of the partner. For a frozen preferred network, we find that
the infection threshold follows the heterogeneous mean field result
and the phase diagram matches the predictions of
the annealed adjacency matrix (AAM) approach. With `blind' adaptations,
although the epidemic threshold remains unchanged, the infection level is
substantially affected, depending on the details of the adaptation. The
`selective' adaptive SIS models are most interesting. Both the threshold and
the level of infection changes, controlled not only by how the adaptations are
implemented but also how often the nodes cut/add links (compared to the time
scales of the epidemic spreading). A simple mean field theory is presented for
the selective adaptations which capture the qualitative and some of the
quantitative features of the infection phase diagram.Comment: 21 pages, 7 figure
Mitigating Epidemics through Mobile Micro-measures
Epidemics of infectious diseases are among the largest threats to the quality
of life and the economic and social well-being of developing countries. The
arsenal of measures against such epidemics is well-established, but costly and
insufficient to mitigate their impact. In this paper, we argue that mobile
technology adds a powerful weapon to this arsenal, because (a) mobile devices
endow us with the unprecedented ability to measure and model the detailed
behavioral patterns of the affected population, and (b) they enable the
delivery of personalized behavioral recommendations to individuals in real
time. We combine these two ideas and propose several strategies to generate
such recommendations from mobility patterns. The goal of each strategy is a
large reduction in infections, with a small impact on the normal course of
daily life. We evaluate these strategies over the Orange D4D dataset and show
the benefit of mobile micro-measures, even if only a fraction of the population
participates. These preliminary results demonstrate the potential of mobile
technology to complement other measures like vaccination and quarantines
against disease epidemics.Comment: Presented at NetMob 2013, Bosto
INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling
We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface.
Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
The role of caretakers in disease dynamics
One of the key challenges in modeling the dynamics of contagion phenomena is
to understand how the structure of social interactions shapes the time course
of a disease. Complex network theory has provided significant advances in this
context. However, awareness of an epidemic in a population typically yields
behavioral changes that correspond to changes in the network structure on which
the disease evolves. This feedback mechanism has not been investigated in
depth. For example, one would intuitively expect susceptible individuals to
avoid other infecteds. However, doctors treating patients or parents tending
sick children may also increase the amount of contact made with an infecteds,
in an effort to speed up recovery but also exposing themselves to higher risks
of infection. We study the role of these caretaker links in an adaptive network
models where individuals react to a disease by increasing or decreasing the
amount of contact they make with infected individuals. We find that pure
avoidance, with only few caretaker links, is the best strategy for curtailing
an SIS disease in networks that possess a large topological variability. In
more homogeneous networks, disease prevalence is decreased for low
concentrations of caretakers whereas a high prevalence emerges if caretaker
concentration passes a well defined critical value.Comment: 8 pages, 9 figure
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