7,534 research outputs found
Controlling nosocomial infection based on structure of hospital social networks
Nosocomial infection raises a serious public health problem, as implied by
the existence of pathogens characteristic to healthcare and hospital-mediated
outbreaks of influenza and SARS. We simulate stochastic SIR dynamics on social
networks, which are based on observations in a hospital in Tokyo, to explore
effective containment strategies against nosocomial infection. The observed
networks have hierarchical and modular structure. We show that healthcare
workers, particularly medical doctors, are main vectors of diseases on these
networks. Intervention methods that restrict interaction between medical
doctors and their visits to different wards shrink the final epidemic size more
than intervention methods that directly protect patients, such as isolating
patients in single rooms. By the same token, vaccinating doctors with priority
rather than patients or nurses is more effective. Finally, vaccinating
individuals with large betweenness centrality is superior to vaccinating ones
with large connectedness to others or randomly chosen individuals, as suggested
by previous model studies. [The abstract of the manuscript has more
information.]Comment: 12 figures, 2 table
Bayesian stochastic blockmodeling
This chapter provides a self-contained introduction to the use of Bayesian
inference to extract large-scale modular structures from network data, based on
the stochastic blockmodel (SBM), as well as its degree-corrected and
overlapping generalizations. We focus on nonparametric formulations that allow
their inference in a manner that prevents overfitting, and enables model
selection. We discuss aspects of the choice of priors, in particular how to
avoid underfitting via increased Bayesian hierarchies, and we contrast the task
of sampling network partitions from the posterior distribution with finding the
single point estimate that maximizes it, while describing efficient algorithms
to perform either one. We also show how inferring the SBM can be used to
predict missing and spurious links, and shed light on the fundamental
limitations of the detectability of modular structures in networks.Comment: 44 pages, 16 figures. Code is freely available as part of graph-tool
at https://graph-tool.skewed.de . See also the HOWTO at
https://graph-tool.skewed.de/static/doc/demos/inference/inference.htm
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
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