Biomedical subjects

Stephen Eubank

Publications and source records attributed to Stephen Eubank.

3 recordsLinked to original sources

PubMed2005

Network based models of infectious disease spread.

It has recently become possible to simulate directly dynamics on very large networks. This paper describes a model of epidemiology on a social network, the Epidemiological Simulation System (EpiSims), and offers general speculation on analyzing disease dynamics on networks. We describe the process of building a realistic social network, describe several different definitions of the network, each useful for certain purposes. Finally, we raise some important questions about structural properties of networks and how they influence dynamics.

Communicable Diseases↗

PubMed2004

Modelling disease outbreaks in realistic urban social networks.

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.

Contact Tracing↗

PubMed1993

Don't bleach chaotic data.

A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is "bleached"), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.

Journal Article↗