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Jeffrey Shaman

Researcher at Columbia University

Publications -  260
Citations -  16741

Jeffrey Shaman is an academic researcher from Columbia University. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 51, co-authored 223 publications receiving 12892 citations. Previous affiliations of Jeffrey Shaman include Columbia University Medical Center & Oregon State University.

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Journal ArticleDOI

Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

Ruiyun Li, +6 more
- 16 Mar 2020 - 
TL;DR: It is estimated that 86% of all infections were undocumented before the 23 January 2020 travel restrictions, which explains the rapid geographic spread of SARS-CoV-2 and indicates that containment of this virus will be particularly challenging.
Journal ArticleDOI

Absolute humidity modulates influenza survival, transmission, and seasonality

Jeffrey Shaman, +1 more
- 03 Mar 2009 - 
TL;DR: Differences in AH provide a single, coherent, more physically sound explanation for the observed variability of IVS, IVT and influenza seasonality in temperate regions and can be further tested through future, additional laboratory, epidemiological and modeling studies.
Journal ArticleDOI

Absolute humidity and the seasonal onset of influenza in the continental United States.

Jeffrey Shaman, +7 more
- 23 Feb 2010 - 
TL;DR: It is demonstrated that variations of absolute humidity explain both the onset of wintertime influenza transmission and the overarching seasonality of this pathogen in temperate regions.
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Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates

James Tamerius, +8 more
- 07 Mar 2013 - 
TL;DR: A simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates is provided.
Journal ArticleDOI

Forecasting seasonal outbreaks of influenza

Jeffrey Shaman, +1 more
- 11 Dec 2012 - 
TL;DR: It is indicated that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak, and confidence in those predictions can be inferred from the spread of the forecast ensemble.