The Mathematics of Infectious Diseases

By H. Hethcote. 2000

6,499 citations recorded by the source corpus.

Subfields: Genetics, Modeling and Simulation, Public Health, Environmental and Occupational Health

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Citations in the corpus

What Stops Social Epidemics? (2011, methodology): “…as contact processes, including adoption of new ideas (Rogers 2003; Bettencourt et al. 2005), spread of infectious disease (Anderson and May 1991; Hethcote 2000) and behaviors (Christakis and Fowler 2007; Christakis and Fowler 2008), computer virus epidemics on the Internet (Castellano and…”
Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models (2008, methodology): “Published online in Articles in Advance March 18, 2008.”
Statistical physics of vaccination (2016, background): “which is the standard mass action formulation of the incidence and the FOI [81, 82].”
The Structure and Function of Complex Networks (2003, background): “) In traditional mathematical epidemiology [23, 26, 192], one then assumes that any susceptible individual has a uniform probability β per unit time of catching the disease from any infective one and that infective individuals recover and become immune at some stochastically constant rate γ.”
Information diffusion and external influence in networks (2012, methodology): “Using acompleteone month trace of Twitter we study how information reaches the nodes of the network.”
A Fractional Order SEIR Model with Density Dependent Death Rate ABSTRACT | FULL TEXT (2011, background): “Since then, many epidemic models have been derived [8].”
Using Mathematical Models in Controlling the Spread of Malaria (2005, neutral): “In the usual Susceptible-Infectious-Recovered-Susceptible (SIRS) or Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model1, the rate 1A good review of standard epidemiological models can be found in Hethcote [34].”
JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses (2020, neutral): “(Huang et al. (2020), Remuzzi and Remuzzi (2020), Zhang et al.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Given an undirected unweighted network with a virus propagation model, at which condition the virus will die out? The answer to the question is the epidemic threshold and it is the minimum level for preventing a viral propagation from dying out quickly [219,286,287].”
Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. (2002, background): “7 March 2005 Ro > 1, then the DFE is unstable and invasion is always possible (see the survey paper by Hethcote [1]).”
Threshold Dynamics for Compartmental Epidemic Models in Periodic Environments (2008, background): “This is because the standard incidence is more suitable for higher population density and the mass action incidence is more suitable when the population density is low [8, 19 ].”