Recurrent outbreaks of influenza and other respiratory viruses continue to affect human health adversely. A number of intervention strategies exist to mitigate the progression of these pathogens, including vaccination, anti-viral therapeutics, public awareness campaigns, face masks, school closure, and quarantine. Public health agency use of these control strategies is guided by their historical effectiveness and implemented in light of the latest estimates of infection incidence, severity, and transmissibility; however, public health officials would be afforded more time to allocate their intervention measures if local outbreak characteristics, e.g., incidence timing, magnitude and duration, could be accurately and reliably forecast. Recent work has shown that some characteristics of seasonal influenza outbreaks can be predicted accurately with lead times of up to 9 weeks. These predictions are generated with a mathematical model of influenza transmission dynamics that has been recursively optimized using an ensemble data assimilation technique and real-time observations of infection incidence. In practice, the data assimilation process entrains the observational estimates of infection incidence into evolving mathematical simulations of pathogen transmission dynamics, and in so doing trains those model simulations, through state space estimation and parameter optimization, to better match the observed unfolding outbreak. Those trained simulations, having been optimized with the most recent observations, are then integrated into the future to generate a distribution of potential disease outcomes. This forecasting framework has been validated for accuracy and reliability, and during the 2012-2013 influenza season was used to generate weekly real-time predictions of influenza peak timing for 108 cities throughout the United States. For this project, we will build on and expand these forecast efforts. Specifically, we will: 1) Work to improve influenza forecast accuracy and reliability through development of multi-model forecast approaches, such as have been used in weather prediction;2) Develop, test and analyze analogous forecast frameworks for other recurrent respiratory pathogens, such as rotavirus and respiratory syncytial virus;3) Establish a dedicated operation center for maintaining, running and disseminating real-time weekly forecasts of influenza and other respiratory viruses;and 4) Work with public health officials in New York City, and, using their more detailed syndromic surveillance, explore the potential for more granular, borough or neighborhood-scale forecast of influenza and other viruses. These efforts will lead to an improved understanding of the benefits and limits of respiratory disease prediction, and the intelligent interpretation and incorporation of real-time forecasts in health response decision-making.

Public Health Relevance

In the last few years, new methods have been introduced to epidemiological modeling that enable the accurate forecasting of infectious disease. For this project, we will expand these disease prediction capabilities and generate operational real-time forecasts of influenza and other respiratory pathogens. Specifically we will: 1) develop multi-model ensemble forecast methods;2) establish a dedicated operational center for maintaining, running and disseminating the forecasts in real time through a free, user- friendly web portal;and 3) work with pubic health agencies and officials to ensure that the forecasts and web portal meet their needs.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01GM110748-01
Application #
8703891
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Sheeley, Douglas
Project Start
2014-09-01
Project End
2019-06-30
Budget Start
2014-09-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10032
Reis, Julia; Shaman, Jeffrey (2016) Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States. PLoS Comput Biol 12:e1005133
Heaney, Alexandra; Little, Eliza; Ng, Sophia et al. (2016) Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality. Ann N Y Acad Sci 1382:31-43
Biggerstaff, Matthew; Alper, David; Dredze, Mark et al. (2016) Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge. BMC Infect Dis 16:357
Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey (2016) Superensemble forecasts of dengue outbreaks. J R Soc Interface 13:
Little, Eliza; Campbell, Scott R; Shaman, Jeffrey (2016) Development and validation of a climate-based ensemble prediction model for West Nile Virus infection rates in Culex mosquitoes, Suffolk County, New York. Parasit Vectors 9:443
Nguyen, Jennifer L; Yang, Wan; Ito, Kazuhiko et al. (2016) Seasonal Influenza Infections and Cardiovascular Disease Mortality. JAMA Cardiol 1:274-81
Alexander, Kathleen A; Sanderson, Claire E; Marathe, Madav et al. (2015) What factors might have led to the emergence of Ebola in West Africa? PLoS Negl Trop Dis 9:e0003652
Yang, Wan; Zhang, Wenyi; Kargbo, David et al. (2015) Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone. J R Soc Interface 12:
Tamerius, James; Viboud, Cécile; Shaman, Jeffrey et al. (2015) Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009. PLoS Comput Biol 11:e1004337
Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y et al. (2015) Forecasting Influenza Epidemics in Hong Kong. PLoS Comput Biol 11:e1004383

Showing the most recent 10 out of 17 publications