Collaborative Hubs: Making the Most of Predictive Epidemic Modeling
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Reich, N.G, et al. Collaborative Hubs: Making the Most of Predictive Epidemic Modeling. American Public Health Association Inc., 2022. https://doi.org/10.17615/12ks-em05APA
Reich, N., Lessler, J., Funk, S., Viboud, C., Vespignani, A., Tibshirani, R., Shea, K., Schienle, M., Runge, M., Rosenfeld, R., Ray, E., Niehus, R., Johnson, H., Johansson, M., Hochheiser, H., Gardner, L., Bracher, J., Borchering, R., & Biggerstaff, M. (2022). Collaborative Hubs: Making the Most of Predictive Epidemic Modeling. American Public Health Association Inc. https://doi.org/10.17615/12ks-em05Chicago
Reich, N.G, J Lessler, S Funk, C Viboud, A Vespignani, R.J Tibshirani, K Shea et al. 2022. Collaborative Hubs: Making the Most of Predictive Epidemic Modeling. American Public Health Association Inc.. https://doi.org/10.17615/12ks-em05- Creator
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Reich, N.G
- Other Affiliation: Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, United States
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Lessler, J
- Affiliation: Gillings School of Global Public Health, Department of Epidemiology
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Funk, S
- Other Affiliation: Centre for Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Viboud, C
- Other Affiliation: Fogarty International Center, National Institutes of Health, Bethesda, MD, United States
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Vespignani, A
- Other Affiliation: Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States
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Tibshirani, R.J
- Other Affiliation: Department of Statistics & Data Science and the Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, United States
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Shea, K
- Other Affiliation: Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, United States
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Schienle, M
- Other Affiliation: Chair of Statistics and Econometrics, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Runge, M.C
- Other Affiliation: Eastern Ecological Science Center, US Geological Survey, Laurel, MD, United States
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Rosenfeld, R
- Other Affiliation: Machine Learning Department, Carnegie Mellon University, United States
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Ray, E.L
- Other Affiliation: Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, United States
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Niehus, R
- Other Affiliation: European Centre for Disease Prevention and Control, Solna, Sweden
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Johnson, H.C
- Other Affiliation: European Centre for Disease Prevention and Control, Solna, Sweden
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Johansson, M.A
- Other Affiliation: COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, United States
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Hochheiser, H
- Other Affiliation: Department of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
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Gardner, L
- Other Affiliation: Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, United States
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Bracher, J
- Other Affiliation: Chair of Statistics and Econometrics, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Borchering, R.K
- Other Affiliation: Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, United States
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Biggerstaff, M
- Other Affiliation: COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, United States
- Abstract
- The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public respond to infectious disease crises. Early in the pandemic, models were used to estimate the true number of infections. Later, they estimated key parameters, generated short-term forecasts of outbreak trends, and quantified possible effects of interventions on the unfolding epidemic. In contrast to the coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many research institutions. Differences in modeling approaches led to contrasting results, contributing to confusion in public perception of the pandemic. Efforts to coordinate modeling efforts in so-called “hubs” have provided governments, healthcare agencies, and the public with assessments and forecasts that reflect the consensus in the modeling community. This has been achieved by openly synthesizing uncertainties across different modeling approaches and facilitating comparisons between them.
- Date of publication
- 2022
- Keyword
- DOI
- Identifier
- Resource type
- Article
- Rights statement
- In Copyright
- Journal title
- American Journal of Public Health
- Journal volume
- 112
- Journal issue
- 6
- Page start
- 839
- Page end
- 842
- Language
- English
- Funder
- National Science Foundation, NSF: DEB-1908538, DEB-1911962, DEB-2028301, DEB-2126278
- Helmholtz Association: U24GM132013
- Wellcome Trust, WT: 210758/Z/18/Z, CDC-HHS-6U01IP001137-01, NU38OT000297
- National Institute of General Medical Sciences, NIGMS: R35GM119582
- Centers for Disease Control and Prevention, CDC: U01 IP001122-01
- Council of State and Territorial Epidemiologists, CSTE
- ISSN
- 0090-0036
- Publisher
- American Public Health Association Inc.
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