Collaborative hubs: Making the most of predictive epidemic modeling
American Journal of Public Health
By: Nicholas G. Reich, Justin Lessler, Sebastian Funk, Cecile Viboud, Alessandro Vespignani, Ryan J Tibshirani, Katriona Shea, Melanie Schienle, Michael C. Runge, Roni Rosenfeld, Evan L Ray, Rene Niehus, Helen C Johnson, Michael A Johansson, Harry Hochheiser, Lauren Gardner, Johannes Bracher, Rebecca K. Borchering, and Matthew Biggerstaff
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Abstract
The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public understand and respond to infectious disease crises. In the early days of the COVID-19 pandemic, models were used first to estimate the true number of infections, then to provide estimates of key parameters, to generate short-term forecasts of outbreak trends, and to quantify the possible effects of different interventions on the unfolding epidemic. In contrast to the central coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many individual research institutions and academic groups. Differences in modeling approaches and assumptions of each individual effort led to contrasting results that at times contributed to confusion in public perception of the pandemic. For this reason, recent efforts to publicly coordinate modeling efforts in so-called “hubs” have provided governments, public health partners, and the public with assessments and forecasts that reflect the consensus (or lack thereof) in the modeling community. This has been achieved by openly and transparently synthesizing uncertainties across different individual modeling approaches and facilitating comparisons between them.
Suggested Citation
Reich, N.G., Lessler, J., Funk, S., Viboud, C., Vespignani, A., Tibshirani, R.J., Shea, K., Schienle, M., Runge, M.C., Rosenfeld, R., Ray, E.L., Niehus, R., Johnson, H.C., Johansson, M.A., Hochheiser, H., Gardner, L., Bracher, J., Borchering, R.K., and Biggerstaff, M., 2022, Collaborative hubs: Making the most of predictive epidemic modeling: American Journal of Public Health, v. 112, p. 839-842, https://doi.org/10.2105/AJPH.2022.306831.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Collaborative hubs: Making the most of predictive epidemic modeling |
| Series title | American Journal of Public Health |
| DOI | 10.2105/AJPH.2022.306831 |
| Volume | 112 |
| Year Published | 2022 |
| Language | English |
| Publisher | American Public Health Association |
| Contributing office(s) | Patuxent Wildlife Research Center, Eastern Ecological Science Center |
| Description | 4 p. |
| First page | 839 |
| Last page | 842 |