Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation
By Odo Diekmann, J. Heesterbeek. 2000
1,225 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/17eebb7000913a7d8ad8e5eef6b3edfbdaaa0f31
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Citations in the corpus
- Statistical physics of vaccination (2016, background): “Notably, no current vaccine is entirely effective.”
- Epidemic outbreaks in complex heterogeneous networks (2001, background): “These results highlight the study of epidemic models in complex networks as potentially relevant also in human and animal epidemiology [23], as confirmed recently by the experimental observation that the web architecture of sexual contacts is best described by a scale-free topology in which…”
- Dynamic behaviour for a nonautonomous heroin epidemic model with time delay (2011, unknown): “(see [1, 2, 4, 8, 10, 11]).”
- Identifying influential spreaders in complex networks (2010, unknown): “To study the spreading process we apply the Susceptible-Infectious-Recovered (SIR) and Susceptible-Infectious-Susceptible (SIS) models [2, 3, 18] on the above networks (see Methods section).”
- Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks (2015, unknown): “[38] O.”
- Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy (2020, methodology): “ is of paramount importance to plan eective control strategies and determine how said strategies impact the course of the epidemic. Predictive mathematical models have been formulated over the years [16, 17, 18, 19], including the SIR model, a simple and widely-used deterministic model for human-to-”
- Epidemic dynamics in finite size scale-free networks. (2002, methodology): “The SIS model does not take into account the possibility of individuals removal due to death or acquired immunization [20], and individuals run stochastically through the cycle susceptible → infected → susceptible.”