Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza

By Cécile Viboud, OTTAR N. Bjørnstad, David L. Smith, L. Simonsen, Mark A. Miller, Bryan T. Grenfell. Science, 2006

918 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/16c987f43802649a76ad60bc81104c5d93164602

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Marc A. Suchard
2. Alexei J. Drummond
3. Andrew Rambaut
4. Philippe Lemey
5. Cécile Viboud
6. Alessandro Vespignani
7. Sergey Kryazhimskiy
8. Edward L. Ionides

In Global and Planetary Change

1. Vincent D. Blondel
2. Marta C. González
3. Edward L. Ionides

In Modeling and Simulation

1. Alessandro Vespignani
2. Vittoria Colizza
3. Marta C. González
4. José J. Ramasco
5. Cécile Viboud
6. M. Keeling
7. Jeffrey Shaman
8. Vincent D. Blondel

Citations in the corpus

• Multiscale mobility networks and the spatial spreading of infectious diseases (2009, background): “(17), although the spatial decay has a completely different form.”
• Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales (2013, methodology): “All observed ILI weekly proportions were analyzed with a traditional Serfling regression approach to establish weekly expected baselines and estimate the ‘‘excess’’ ILI proportions attributable to influenza and identify epidemic periods ([28–33]; Supporting Information).”
• Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. (2007, background): “…provides a framework for the understanding of the evidence collected on the interplay between travel and global spread of infectious diseases (Viboud et al. , 2006) and the poor effectiveness of travel restrictions in the containment of epidemics (Cooper et al. , 2006; Hollingsworth et al. ,…”
• Networks and the Epidemiology of Infectious Disease (2010, methodology): “Heffernan et al. [151] summarise various methods for estimating R0 from epidemiological data based on endemic equilibrium, average age at infection, epidemic final size, and intrinsic growth rate [114, 152, 153].”
• On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “We focus on rapid directly transmitted infections, such as influenza-like-illnesses, for which daily regular movements of individuals for commuting purposes were found to correlate well with the observed regional spread [12,13].”