Travelling waves and spatial hierarchies in measles epidemics
By Bryan T. Grenfell, OTTAR N. Bjørnstad, J. Kappey. Nature, 2001
967 citations recorded by the source corpus.
Subfields: Ecology, Modeling and Simulation, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/d12936031f58b8b3cbc0ddde49f71899c8d05194
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- W. Koenig
- OTTAR N. Bjørnstad
- Andrew M. Liebhold
- Edward L. Ionides
- Derek M. Johnson
- Richard S. Ostfeld
- M. Mcmanus
- Felicia Keesing
In Modeling and Simulation
- Nicholas C. Grassly
- Andrew M. Liebhold
- Edward C. Holmes
- Yong‐Zhen Zhang
- Erwin Frey
- W. Koenig
- Edward L. Ionides
- Aaron M. King
In Public Health, Environmental and Occupational Health
- Richard S. Ostfeld
- Andrew M. Liebhold
- Edward C. Holmes
- Yong‐Zhen Zhang
- Erwin Frey
- Felicia Keesing
- W. Koenig
- Jianhong Wu
Citations in the corpus
- Mathematical models of infectious disease transmission (2008, background): “If communities are distributed across a landscape and infection is introduced to just one community, however, travelling waves of infection can occur 20, 47 , as shown for the spread of measles before the introduction of vaccination in the United Kingdom 48 .”
- Time-dependent spectral analysis of epidemiological time-series with wavelets (2007, methodology): “In 2001, Grenfell and collaborators published a paper that analysed synchrony patterns of measles in the UK (Grenfell et al. 2001).”
- Traveling waves of diffusive predator-prey systems: Disease outbreak propagation (2012, background): “Other studies include [25] for measles epidemics; [11] for Lyme disease; [56] and [41] for dengue; and [1] and [2] for Hantavirus infection in deer mice.”
- Mathematical models to characterize early epidemic growth: A Review (2016, background): “Indeed, past modeling efforts have incorporated mechanisms to account for slower than exponential growth patterns including models that gradually mitigate the transmission rate over time [20–23] or incorporate phenomenological parameters to capture non-homogeneous population mixing [24,25] and model”
- Evolutionary game theory: Theoretical concepts and applications to microbial communities (2010, background): “Examples range from the celebrated Belousov-Zhabotinsky reaction [94] (spiralling patterns) and many other chemical reactions [95], to epidemic outbreaks (traveling waves) [96,97], excitable media [95,98], and calcium signalling within single cells [99–101].”
- Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, background): “Some must modify the model, discretizing time to the scale of observations (Finkenstädt & Grenfell 2000; Grenfell et al. 2001, 2002; Bjørnstad et al. 2002; Finkenstädt et al. 2002; Clark & Bjørnstad 2004; Koelle & Pascual 2004).”
- Spatial epidemiology: an emerging (or re-emerging) discipline. (2005, background): “Before vaccination was common, measles epidemics in England and Wales tended to peak every other year and traveled in waves from large cities to small surrounding towns [ 6 ].”
- Landscape scale, heterogeneity, and the viability of Serengeti grazers (2005, methodology): “…1987; De Roos et al. 1991; Hassell et al. 1991; Neubert et al. 1995; Lima & Zollner 1996; Ranta et al. 1997; Lambin et al. 1998; Blasius et al. 1999; Grenfell et al. 2001), spatiallyexplicit landscape models have rarely been used to consider long-term population viability (Morris & Doak 2002).”
- Synchronization in complex networks (2008, background): “Examples include neural networks (Felleman and Van Essen, 1991; Scannell et al., 1999), airport networks (Barrat et al., 2004) and the structure of the networks characterizing epidemic outbreaks in different cities (Grassly et al., 2005; Grenfell et al., 2001).”