Forecast Work
This page lists some of the papers and archived postings developed for a
project entitled Influenza Outbreak Prediction: Applying Data
Assimilation Methodologies to Make Skillful Forecasts of an Inherently
Chaotic, Nonlinear System, which is funded through the NIH (NIGMS)/NSF
(DMS) joint initiative to support research at the interface of the
biological and mathematical sciences. The title is a bit of a mouthful
(as is the funding program name); basically, we are using data
assimilation methods, as commonly employed in numerical weather
prediction, in conjunction with real-time observations of influenza
incidence to train and optimize model simulations of influenza
transmission dynamics on the fly and then use those optimized models to
generate real-time forecasts of influenza outcomes.
Additional funding comes from the Biomedical Advanced Research and
Development Authority of the Department of Health and Human Services, as
well as the Models of Infectious Disease Agent Study (MIDAS) program of
the the NIH.
I'm also thrilled to announce that my team (Wan Yang, Alicia
Karspeck, Marc Lipsitch and I) recently won the 'CDC Predict the Flu
Challenge'. The prize announcement can be found here: - CDC
Announcement
Accepted/In Press
- Yang, W., M. Lipsitch and J. Shaman: Inference of
seasonal and pandemic influenza transmission dynamics using big
surveillance data. Proceedings of the National Academy of
Sciences.
2014
- Shaman, J., W. Yang and S. Kandula, 2014: Inference
and Forecast of the Current West African Ebola Outbreak in Guinea, Sierra
Leone and Liberia. PLOS Currents Outbreaks, 2014 Oct 31.
Edition 1. doi:
10.1371/currents.outbreaks.3408774290b1a0f2dd7cae877c8b8ff6
- Yang, W., A. Karspeck and J. Shaman, 2014:
Comparison of filtering methods for the modeling and retrospective
forecasting of influenza epidemics. PLOS Computational Biology,
10(4): e1003583, doi:10.1371/journal.pcbi.1003583
- Chretien, J.-P., D. George, J. Shaman, R. A. Chitale and F. E.
McKenzie, 2014: Influenza
forecasting in human populations: a scoping review. PLOS
ONE, 9(4): e94130. doi:10.1371/journal.pone.0094130
- Yang, W. and J. Shaman, 2014: A simple modification for improving
inference of non-linear dynamical systems. ArXiv:1403.6804
[stat.ME].
2013
- Shaman, J, A. Karspeck, W. Yang, J. Tamerius, and M. Lipsitch,
2013:
Real-Time Influenza Forecasts during the 2012-2013 Season. Nature
Communications, 4: Article Number 2837,
doi:10.1038/ncomms3837.
- Shaman, J, A. Karspeck, and M. Lipsitch, 2013: Week 1 Influenza Forecast for the
2012-2013 U.S. Season ArXiv:1301.3110 [q-bio.PE].
- Shaman, J, A. Karspeck, and M. Lipsitch, 2013: Week 52 Influenza Forecast for the
2012-2013 U.S. Season ArXiv:1301.1111 [q-bio.PE].
2012
- Shaman, J, A. Karspeck, and M. Lipsitch, 2012: Week 51 Influenza Forecast for the
2012-2013 U.S. Season ArXiv:1212.6678 [q-bio.PE].
- Shaman, J, A. Karspeck, and M. Lipsitch, 2012: Week 50 Influenza Forecast for the
2012-2013 U.S. Season ArXiv:1212.5750 [q-bio.PE].
- Shaman, J, A. Karspeck, and M. Lipsitch, 2012: Week 49 Influenza Forecast for the
2012-2013 U.S. Season ArXiv:1212.4678 [q-bio.PE].
- Shaman, J. and A. Karspeck, 2012: Forecasting Seasonal
Outbreaks of Influenza. Proceedings of the National Academy of
Sciences, 109(50): 20425-20430,
doi:10.1073/pnas.1208772109.
jls106@columbia.edu
Jeffrey Shaman
Department of Environmental Health Sciences
Mailman School of Public Health
Columbia University
722 West 168th Street
Rosenfield Building
Room 1104C
New York, NY
10032