83,776 research outputs found
The Opportunistic Transmission of Wireless Worms between Mobile Devices
The ubiquity of portable wireless-enabled computing and communications
devices has stimulated the emergence of malicious codes (wireless worms) that
are capable of spreading between spatially proximal devices. The potential
exists for worms to be opportunistically transmitted between devices as they
move around, so human mobility patterns will have an impact on epidemic spread.
The scenario we address in this paper is proximity attacks from fleetingly
in-contact wireless devices with short-range communication range, such as
Bluetooth-enabled smart phones. An individual-based model of mobile devices is
introduced and the effect of population characteristics and device behaviour on
the outbreak dynamics is investigated. We show through extensive simulations
that in the above scenario the resulting mass-action epidemic models remain
applicable provided the contact rate is derived consistently from the
underlying mobility model. The model gives useful analytical expressions
against which more refined simulations of worm spread can be developed and
tested.Comment: Submitted for publicatio
Individual-based lattice model for spatial spread of epidemics
We present a lattice gas cellular automaton (LGCA) to study spatial and
temporal dynamics of an epidemic of SIR (susceptible-infected-removed) type.
The automaton is fully discrete, i.e. space, time and number of individuals are
discrete variables. The automaton can be applied to study spread of epidemics
in both human and animal populations. We investigate effects of spatial
inhomogeneities in initial distribution of infected and vaccinated populations
on the dynamics of epidemic of SIR type. We discuss vaccination strategies
which differ only in spatial distribution of vaccinated individuals. Also, we
derive an approximate, mean-field type description of the automaton, and
discuss differences between the mean-field dynamics and the results of LGCA
simulation.Comment: 13 pages, 5 figure
Real-time growth rate for general stochastic SIR epidemics on unclustered networks
Networks have become an important tool for infectious disease epidemiology.
Most previous theoretical studies of transmission network models have either
considered simple Markovian dynamics at the individual level, or have focused
on the invasion threshold and final outcome of the epidemic. Here, we provide a
general theory for early real-time behaviour of epidemics on large
configuration model networks (i.e. static and locally unclustered), in
particular focusing on the computation of the Malthusian parameter that
describes the early exponential epidemic growth. Analytical, numerical and
Monte-Carlo methods under a wide variety of Markovian and non-Markovian
assumptions about the infectivity profile are presented. Numerous examples
provide explicit quantification of the impact of the network structure on the
temporal dynamics of the spread of infection and provide a benchmark for
validating results of large scale simulations.Comment: 45 pages, 8 figures, submitted to Mathematical Biosciences on
29/11/2014; Version 2: resubmitted on 15/04/2015; accepted on 17/04/2015.
Changes: better explanations in introduction; restructured section 3.3 (3.3.3
added); section 6.3.1 added; more precise terminology; typos correcte
Dynamics of multi-stage infections on networks
This paper investigates the dynamics of infectious diseases with a nonexponentially distributed infectious period. This is achieved by considering a multistage infection model on networks. Using pairwise approximation with a standard closure, a number of important characteristics of disease dynamics are derived analytically, including the final size of an epidemic and a threshold for epidemic outbreaks, and it is shown how these quantities depend on disease characteristics, as well as the number of disease stages. Stochastic simulations of dynamics on networks are performed and compared to output of pairwise models for several realistic examples of infectious diseases to illustrate the role played by the number of stages in the disease dynamics. These results show that a higher number of disease stages results in faster epidemic outbreaks with a higher peak prevalence and a larger final size of the epidemic. The agreement between the pairwise and simulation models is excellent in the cases we consider
Persistence, extinction and spatio-temporal synchronization of SIRS cellular automata models
Spatially explicit models have been widely used in today's mathematical
ecology and epidemiology to study persistence and extinction of populations as
well as their spatial patterns. Here we extend the earlier work--static
dispersal between neighbouring individuals to mobility of individuals as well
as multi-patches environment. As is commonly found, the basic reproductive
ratio is maximized for the evolutionary stable strategy (ESS) on diseases'
persistence in mean-field theory. This has important implications, as it
implies that for a wide range of parameters that infection rate will tend
maximum. This is opposite with present results obtained in spatial explicit
models that infection rate is limited by upper bound. We observe the emergence
of trade-offs of extinction and persistence on the parameters of the infection
period and infection rate and show the extinction time having a linear
relationship with respect to system size. We further find that the higher
mobility can pronouncedly promote the persistence of spread of epidemics, i.e.,
the phase transition occurs from extinction domain to persistence domain, and
the spirals' wavelength increases as the mobility increasing and ultimately, it
will saturate at a certain value. Furthermore, for multi-patches case, we find
that the lower coupling strength leads to anti-phase oscillation of infected
fraction, while higher coupling strength corresponds to in-phase oscillation.Comment: 12page
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