title: Network theory and SARS: predicting outbreak diversity.
abstract: 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.
authors.affiliation: Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, 1 University Station C0930, Austin, TX 78712, USA. laurenmeyers@mail.utexas.edu
authors.forename: Lauren Ancel
authors.initials: LA
authors.lastname: Meyers
authors.forename: Babak
authors.initials: B
authors.lastname: Pourbohloul
authors.forename: M E J
authors.initials: ME
authors.lastname: Newman
authors.forename: Danuta M
authors.initials: DM
authors.lastname: Skowronski
authors.forename: Robert C
authors.initials: RC
authors.lastname: Brunham
mesh_terms: D000328:Adult; D002648:Child; D003198:Computer Simulation; D004196:Disease Outbreaks; D006801:Humans; D008954:Models, Biological; D011634:Public Health / Q000379:methods; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D013269:Stochastic Processes; D014504:Urban Health