Network theory and SARS: predicting outbreak diversity

By Lauren Ancel Meyers, B. Pourbohloul, Mark Newman, D. Skowronski, R. Brunham. Journal of Theoretical Biology, 2004

765 citations recorded by the source corpus.

Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional “compartmental” modeling in epidemiology, however, assumes that population groups are fully mixed, that is, every individual has an equal chance of spreading the disease to every other. Applications of compartmental models to Severe Acute Respiratory Syndrome (SARS) resulted in estimates of the fundamental quantity called the basic reproductive number R0—the number of new cases of SARS resulting from a single initial case—above one, implying that, without public health intervention, most outbreaks should spark large-scale epidemics. Here we compare these predictions to the early epidemiology of SARS. We apply the methods of contact network epidemiology to illustrate that for a single value of R0, any two outbreaks, even in the same setting, may have very different epidemiological outcomes. We offer quantitative insight into the heterogeneity of SARS outbreaks worldwide, and illustrate the utility of this approach for assessing public health strategies.

Subfields: Modeling and Simulation, Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/01e01d2af541ccd2f440c3947806d8a8b5a68205

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Citations in the corpus

Coupled disease–behavior dynamics on complex networks: A review (2015, background): “Adaptive network Multilayer networks Social network topology properties determining disease status or (and) individual behavior [93,117,118,131,134,141, 142]”
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Superspreading and the effect of individual variation on disease emergence (2005, background): “This work must be integrated with established theory of sexually transmitted diseases and social networks, where high-risk groups exert nonlinear inﬂuence on R 0 because contact rates affect infectiousness and susceptibility equally 3,4,12,13,22 .”
Connectivity networks reveal the risks of crown-of-thorns starfish outbreaks on the Great Barrier Reef (2014, background): “describe the spread of communicable diseases (Meyers et al. 2005).”
Systems Science Methods in Public Health (2012, neutral): “First, it was during the early years of the HIV epidemic [followed by work on severe acute respiratory syndrome (SARS) and other infectious diseases] that epidemiologists started to employ network analytic methods as a new way to chart the spread of a disease and to plan how to counter disease outbr”
Social network theory: new insights and issues for behavioral ecologists (2009, background): “Social learning and information flow In studies on humans, one major use of SNT involves analyses of how SNS influences flow, e.g., of information, innovation, or disease (Gould and Fernandez 1989; Meyers et al. 2005; Björneborn 2006; Rothenberg and Muth 2007).”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “This approach and others have been used to demonstrate the dramatic effects that the degree distribution can have on disease spread in a population: homogeneous populations (with low variance degree distributions) are characterized by a high epidemic threshold and experience a lag before reaching th”
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Statistical physics of vaccination (2016, background): “For example, Meyers et al [251] generates a network model to characterize the spread of SARS in Vancouver, British Columbia.”
Compartmental Models in Epidemiology (2008, background): “4 of this volume and described in [28,30,33], is another approach.”
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A high-resolution human contact network for infectious disease transmission (2010, background): “Recent work on disease spread on networks has identified the relationship between R0, the network degree distribution, and the average probability that an infectious individual transmits the disease to a susceptible individual, T (18, 26).”
Graph theory and networks in Biology. (2006, background): “Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.”