The Mathematical Theory of Infectious Diseases and Its Applications

By A. Grey. 1977

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Citations in the corpus

What Stops Social Epidemics? (2011, background): “These models assume that everyone is in contact with everyone else in the population, and rate of infec- tion and recovery is uniform (Bailey 1975).”
Gossip-based computation of aggregate information (2003, background): “The dynamics of information spread bear a resemblance to the spread of an epidemic [5, 10], and lead to high faulttolerance and “self-stabilization” [8, 10, 34].”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “These early methods are summarised by Bailey [126].”
Information source detection in the SIR model: A sample path based approach (2012, methodology): “In this paper, we adopt the Susceptible-Infected-Recovered (SIR) model, a standard model of epidemics [6], [7].”
Modeling Radicalization Phenomena in Heterogeneous Populations (2015, background): “For instance, the SIS [51] model considers a two-state population where agents may vary their state from S (i.”
Influentials, Networks, and Public Opinion Formation (2007, background): “Nor do other kinds of diffusion models (Bailey 1975; Dodds and Watts 2005; Dodson and Muller 1978; Granovetter 1978; Shrager, Hogg and Huberman 1987; Young 2006) require opinion leaders or any kind of special individuals in order to generate S-shaped diffusion curves, which are characteristic of…”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “The basic SIR model, like many other epidemiological models, begins with an assumption that individuals form a single large population and that they all mix randomly with one another [1,4,39].”
Modeling Information Propagation with Survival Theory (2013, background): “, 2006) or the propagation of a contagious disease over social network of individuals (Bailey, 1975).”
Connectivity and inference problems for temporal networks (2000, background): “The study of epidemics - - the spread of infectious diseases - - is a well-developed area of applied mathematics [1].”
Design patterns from biology for distributed computing (2006, background): “Examples include growth processes, signal propagation in certain neural networks [Arbib et al. 1997], epidemic spreading [Bailey 1975], or proliferation processes in the immune system [Janeway et al. 2001].”
Information diffusion through blogspace (2004, background): “(See, for example, the book of Bailey [4] for some of the extensive work in this .eld.)”
Limiting the spread of misinformation in social networks (2011, background): “There are many studies on the spread of infections and immunization [41, 3, 25].”
Network theory and SARS: predicting outbreak diversity (2004, background): “…groups capture greater behavioral heterogeneity (Hethcote and Yorke, 1984); stochastic approaches including stochastic compartmental models (Bailey, 1975), branching process models (Becker, 1977; Farrington et al., 2003), dyad models (Keeling et al., 1997; Ferguson and Garnett, 2000), and…”