Nicholas Generous
Los Alamos National Laboratory
26 Papers
45 Citations
Nicholas Generous is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Outbreak & Data sharing. The author has an hindex of 11, co-authored 26 publications.
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Papers
Enhancing disease surveillance with novel data streams: challenges and opportunities
Benjamin M. Althouse,Samuel V. Scarpino,Lauren Ancel Meyers,Lauren Ancel Meyers,John W. Ayers,Marisa Bargsten,Joan Baumbach,John S. Brownstein,John S. Brownstein,John S. Brownstein,Lauren Castro,Hannah E. Clapham,Derek A. T. Cummings,Sara Y. Del Valle,Stephen Eubank,Geoffrey Fairchild,Lyn Finelli,Nicholas Generous,Dylan B. George,David R. Harper,Laurent Hébert-Dufresne,Michael A. Johansson,Kevin J. Konty,Marc Lipsitch,Gabriel J. Milinovich,Joseph D. Miller,Elaine O. Nsoesie,Elaine O. Nsoesie,Donald R. Olson,Michael J. Paul,Philip M. Polgreen,Reid Priedhorsky,Jonathan M. Read,Isabel Rodriguez-Barraquer,Derek J. Smith,Christian Stefansen,David L. Swerdlow,Deborah L. Thompson,Alessandro Vespignani,Amy Wesolowski +39 more
TL;DR: This paper outlines a conceptual framework for integrating NDS into current public health surveillance and presents the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical.
Global disease monitoring and forecasting with Wikipedia.
TL;DR: A research agenda designed to produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art is closed.
Forecasting the 2013-2014 influenza season using Wikipedia.
Kyle S. Hickmann,Geoffrey Fairchild,Reid Priedhorsky,Nicholas Generous,James M. Hyman,Alina Deshpande,Sara Y. Del Valle +6 more
TL;DR: This work combines modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza, and adjusts the initialization and parametrization of a disease model to determine systematic model bias.
•Proceedings Article
Worldwide Influenza Surveillance through Twitter
Michael J. Paul,Mark Dredze,David A. Broniatowski,Nicholas Generous +3 more
- 01 Apr 2015
TL;DR: It is shown that incorporating Twitter data into a strong autoregressive baseline reduces mean squared error in 80 to 100 percent of locations depending on the lag, with larger improvements when reporting delays are longer.
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Resources to Discover and Use Short Linear Motifs in Viral Proteins
Peter T. Hraber,Paul E. O'Maille,Andrew Silberfarb,Katie Davis-Anderson,Nicholas Generous,Benjamin H. McMahon,Jeanne M. Fair +6 more
TL;DR: This work surveys viral uses of SLiMs to mimic host proteins, and information resources available for motif discovery, and investigates potential use in synthetic biology, such as better immunogens and therapies, but may also present biosecurity challenges.
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