1,718 research outputs found
Cost-efficient vaccination protocols for network epidemiology
We investigate methods to vaccinate contact networks -- i.e. removing nodes
in such a way that disease spreading is hindered as much as possible -- with
respect to their cost-efficiency. Any real implementation of such protocols
would come with costs related both to the vaccination itself, and gathering of
information about the network. Disregarding this, we argue, would lead to
erroneous evaluation of vaccination protocols. We use the
susceptible-infected-recovered model -- the generic model for diseases making
patients immune upon recovery -- as our disease-spreading scenario, and analyze
outbreaks on both empirical and model networks. For different relative costs,
different protocols dominate. For high vaccination costs and low costs of
gathering information, the so-called acquaintance vaccination is the most cost
efficient. For other parameter values, protocols designed for query-efficient
identification of the network's largest degrees are most efficient
Immunization for complex network based on the effective degree of vertex
The basic idea of many effective immunization strategies is first to rank the
importance of vertices according to the degrees of vertices and then remove the
vertices from highest importance to lowest until the network becomes
disconnected. Here we define the effective degrees of vertex, i.e., the number
of its connections linking to un-immunized nodes in current network during the
immunization procedure, to rank the importance of vertex, and modify these
strategies by using the effective degrees of vertices. Simulations on both the
scale-free network models with various degree correlations and two real
networks have revealed that the immunization strategies based on the effective
degrees are often more effective than those based on the degrees in the initial
network.Comment: 16 pages, 5 figure
Immunization strategies for epidemic processes in time-varying contact networks
Spreading processes represent a very efficient tool to investigate the
structural properties of networks and the relative importance of their
constituents, and have been widely used to this aim in static networks. Here we
consider simple disease spreading processes on empirical time-varying networks
of contacts between individuals, and compare the effect of several immunization
strategies on these processes. An immunization strategy is defined as the
choice of a set of nodes (individuals) who cannot catch nor transmit the
disease. This choice is performed according to a certain ranking of the nodes
of the contact network. We consider various ranking strategies, focusing in
particular on the role of the training window during which the nodes'
properties are measured in the time-varying network: longer training windows
correspond to a larger amount of information collected and could be expected to
result in better performances of the immunization strategies. We find instead
an unexpected saturation in the efficiency of strategies based on nodes'
characteristics when the length of the training window is increased, showing
that a limited amount of information on the contact patterns is sufficient to
design efficient immunization strategies. This finding is balanced by the large
variations of the contact patterns, which strongly alter the importance of
nodes from one period to the next and therefore significantly limit the
efficiency of any strategy based on an importance ranking of nodes. We also
observe that the efficiency of strategies that include an element of randomness
and are based on temporally local information do not perform as well but are
largely independent on the amount of information available
Uncovering missing links with cold ends
To evaluate the performance of prediction of missing links, the known data
are randomly divided into two parts, the training set and the probe set. We
argue that this straightforward and standard method may lead to terrible bias,
since in real biological and information networks, missing links are more
likely to be links connecting low-degree nodes. We therefore study how to
uncover missing links with low-degree nodes, namely links in the probe set are
of lower degree products than a random sampling. Experimental analysis on ten
local similarity indices and four disparate real networks reveals a surprising
result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J.
Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was
known to be one of the worst indices if the probe set is a random sampling of
all links. We further propose an parameter-dependent index, which considerably
improves the prediction accuracy. Finally, we show the relevance of the
proposed index on three real sampling methods.Comment: 16 pages, 5 figures, 6 table
Invited review: Epidemics on social networks
Since its first formulations almost a century ago, mathematical models for
disease spreading contributed to understand, evaluate and control the epidemic
processes.They promoted a dramatic change in how epidemiologists thought of the
propagation of infectious diseases.In the last decade, when the traditional
epidemiological models seemed to be exhausted, new types of models were
developed.These new models incorporated concepts from graph theory to describe
and model the underlying social structure.Many of these works merely produced a
more detailed extension of the previous results, but some others triggered a
completely new paradigm in the mathematical study of epidemic processes. In
this review, we will introduce the basic concepts of epidemiology, epidemic
modeling and networks, to finally provide a brief description of the most
relevant results in the field.Comment: 17 pages, 13 figure
Rapid decay in the relative efficiency of quarantine to halt epidemics in networks
Several recent studies have tackled the issue of optimal network immunization
by providing efficient criteria to identify key nodes to be removed in order to
break apart a network, thus preventing the occurrence of extensive epidemic
outbreaks. Yet, although the efficiency of those criteria has been demonstrated
also in empirical networks, preventive immunization is rarely applied to
real-world scenarios, where the usual approach is the a posteriori attempt to
contain epidemic outbreaks using quarantine measures. Here we compare the
efficiency of prevention with that of quarantine in terms of the tradeoff
between the number of removed and saved nodes on both synthetic and empirical
topologies. We show how, consistent with common sense, but contrary to common
practice, in many cases preventing is better than curing: depending on network
structure, rescuing an infected network by quarantine could become inefficient
soon after the first infection.Comment: 10 pages, 7 figure
- …