Modeling Infectious Diseases in Humans and Animals

By M. Keeling, P. Rohani. 2007

3,955 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fab4e2bc033cb8bc1a5587b235184c66e4faea2b

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In Modeling and Simulation

In Public Health, Environmental and Occupational Health

Citations in the corpus

"Herd immunity": a rough guide. (2011, background): “B, Cumulative lifetime incidence of infection in unvaccinated individuals as a function of the level of random vaccine coverage of an entire population, as predicted by a simple susceptible-infected-recovered model for a ubiquitous infection with R0 5 3 [13].”
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, background): “Compartmental models in epidemiology describe the stochastic dynamics of infectious diseases as they spread over a population of individuals [47, 25, 22].”
Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015, background): “A mainstay of theoretical epidemiology, the SIR model describes the progress of a contagious, immunizing infection through a population of hosts (Kermack and McKendrick 1927; Anderson and May 1991; Keeling and Rohani 2008).”
Ranavirus Ecology and Evolution: From Epidemiology to Extinction (2015, background): “Thus, the dynamics of ranavirus infection may be similar to those of many other infectious diseases (Keeling and Rohani 2008 ), spreading slowly after introduction because the infection is rare and accelerating as the epidemic builds.”
The Anatomy of a Scientific Rumor (2013, methodology): “In order to model the evolution of the active users over time, we firstly tried to exploit classic susceptibleinfected (SI) models in an unstructured population [41],”
Human mobility: Models and applications (2017, background): “Consequently, the literature in this area is extensive, with dedicated monographs and a number of reviews [337, 338, 339, 340].”
The H-index of a network node and its relation to degree and coreness (2016, background): “Epidemic spreading, one of the most signiﬁcant dynamic behaviours in complex networks 41 , can be used to characterize such real processes as the spreading of infectious diseases 42 , the diffusion of microﬁnance 43 and the propagation of trafﬁc congestion 44 .”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “Readers interested in more details on spatially continuous epidemiological models are encouraged to consult Sattenspiel [44] or Keeling and Rohani [45].”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, methodology): “We use a metapopulation modeling approach [40,41] to perform numerical simulations of epidemic scenarios.”
Dynamic Mode Decomposition with Control (2014, background): “disease leading to an enormous number of phenomenological models [22].”