6,952 research outputs found
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
A stochastic multi-scale model of HIV-1 transmission for decision-making: application to a MSM population.
BackgroundIn the absence of an effective vaccine against HIV-1, the scientific community is presented with the challenge of developing alternative methods to curb its spread. Due to the complexity of the disease, however, our ability to predict the impact of various prevention and treatment strategies is limited. While ART has been widely accepted as the gold standard of modern care, its timing is debated.ObjectivesTo evaluate the impact of medical interventions at the level of individuals on the spread of infection across the whole population. Specifically, we investigate the impact of ART initiation timing on HIV-1 spread in an MSM (Men who have Sex with Men) population.Design and methodsA stochastic multi-scale model of HIV-1 transmission that integrates within a single framework the in-host cellular dynamics and their outcomes, patient health states, and sexual contact networks. The model captures disease state and progression within individuals, and allows for simulation of therapeutic strategies.ResultsEarly ART initiation may substantially affect disease spread through a population.ConclusionsOur model provides a multi-scale, systems-based approach to evaluate the broader implications of therapeutic strategies
FastSIR Algorithm: A Fast Algorithm for simulation of epidemic spread in large networks by using SIR compartment model
The epidemic spreading on arbitrary complex networks is studied in SIR
(Susceptible Infected Recovered) compartment model. We propose our
implementation of a Naive SIR algorithm for epidemic simulation spreading on
networks that uses data structures efficiently to reduce running time. The
Naive SIR algorithm models full epidemic dynamics and can be easily upgraded to
parallel version. We also propose novel algorithm for epidemic simulation
spreading on networks called the FastSIR algorithm that has better average case
running time than the Naive SIR algorithm. The FastSIR algorithm uses novel
approach to reduce average case running time by constant factor by using
probability distributions of the number of infected nodes. Moreover, the
FastSIR algorithm does not follow epidemic dynamics in time, but still captures
all infection transfers. Furthermore, we also propose an efficient recursive
method for calculating probability distributions of the number of infected
nodes. Average case running time of both algorithms has also been derived and
experimental analysis was made on five different empirical complex networks.Comment: 8 figure
Activation thresholds in epidemic spreading with motile infectious agents on scale-free networks
We investigate a fermionic susceptible-infected-susceptible model with
mobility of infected individuals on uncorrelated scale-free networks with
power-law degree distributions of exponents
. Two diffusive processes with diffusion rate of an infected
vertex are considered. In the \textit{standard diffusion}, one of the
nearest-neighbors is chosen with equal chance while in the \textit{biased
diffusion} this choice happens with probability proportional to the neighbor's
degree. A non-monotonic dependence of the epidemic threshold on with an
optimum diffusion rate , for which the epidemic spreading is more
efficient, is found for standard diffusion while monotonic decays are observed
in the biased case. The epidemic thresholds go to zero as the network size is
increased and the form that this happens depends on the diffusion rule and
degree exponent. We analytically investigated the dynamics using quenched and
heterogeneous mean-field theories. The former presents, in general, a better
performance for standard and the latter for biased diffusion models, indicating
different activation mechanisms of the epidemic phases that are rationalized in
terms of hubs or max -core subgraphs.Comment: 9 pages, 4 figure
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
Heterogeneity in the spread and control of infectious disease: consequences for the elimination of canine rabies
Understanding the factors influencing vaccination campaign effectiveness is vital in designing efficient disease elimination programmes. We investigated the importance of spatial heterogeneity in vaccination coverage and human-mediated dog movements for the elimination of endemic canine rabies by mass dog vaccination in Region VI of the Philippines (Western Visayas). Household survey data was used to parameterise a spatially-explicit rabies transmission model with realistic dog movement and vaccination coverage scenarios, assuming a basic reproduction number for rabies drawn from the literature. This showed that heterogeneous vaccination reduces elimination prospects relative to homogeneous vaccination at the same overall level. Had the three vaccination campaigns completed in Region VI in 2010–2012 been homogeneous, they would have eliminated rabies with high probability. However, given the observed heterogeneity, three further campaigns may be required to achieve elimination with probability 0.95. We recommend that heterogeneity be reduced in future campaigns through targeted efforts in low coverage areas, even at the expense of reduced coverage in previously high coverage areas. Reported human-mediated dog movements did not reduce elimination probability, so expending limited resources on restricting dog movements is unnecessary in this endemic setting. Enhanced surveillance will be necessary post-elimination, however, given the reintroduction risk from long-distance dog movements
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