Citing the Forecast Hub

To cite the US COVID-19 Forecast Hub dataset and project as a whole, please cite the dataset descriptor preprint:
Cramer EY, Huang Y, Wang Y, et al. “The United States COVID-19 Forecast Hub dataset.” 2021. medRxiv. (preprint)


@article {Cramer2021-hub-dataset,
	author = {Cramer, Estee Y and Huang, Yuxin and Wang, Yijin and Ray, Evan L and Cornell, Matthew and Bracher, Johannes and Brennen, Andrea and Castro Rivadeneira, Alvaro J and Gerding, Aaron and House, Katie and Jayawardena, Dasuni and Kanji, Abdul H and Khandelwal, Ayush and Le, Khoa and Niemi, Jarad and Stark, Ariane and Shah, Apurv and Wattanachit, Nutcha and Zorn, Martha W and Reich, Nicholas G and US COVID-19 Forecast Hub Consortium},
	title = {The United States COVID-19 Forecast Hub dataset},
	year = {2021},
	doi = {10.1101/2021.11.04.21265886},
	URL = {},
	journal = {medRxiv}

To cite the dataset and GitHub repository directly, we ask that you cite the Data Descriptor paper (see above) but you may also cite or refer to the permanent DOI for the GitHub repo (the DOI is updated by Zenodo when we create a new “release” of this GitHub repository).

To cite research results from the hub, please review the list below and choose the relevant research publication(s).

Research Background

Since 2016, our team has worked closely with the US CDC on forecasting seasonal influenza outbreaks in a project called FluSight. This collaborative research framework has served as a model for setting up the COVID-19 Forecast Hub. Below, we list publications that have emerged from the collaborative work of the COVID-19 Forecast Hub research team, as well as pre-COVID publications.

COVID-19 Forecast Hub publications

Partial list of relevant publications from our team (pre-COVID):

We have published numerous peer-reviewed research articles on infectious disease forecasting that provide a solid foundation for understanding how to best forecast outbreaks. For example, our work has found that there was not a large difference in predictive performance between mechanistic and statistical models of seasonal influenza outbreaks. We also showed modest but measurable improvements in accuracy when using an ensemble forecast to forecast influenza.