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
- Cramer EY, Ray EL, Lopez VK, et al. “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US.” 2021. medRxiv. (preprint)
- Logan C. Brooks, Evan L. Ray, Jacob Bien, Johannes Bracher, Aaron Rumack, Ryan J. Tibshirani, Nicholas G. Reich. “Comparing ensemble approaches for short-term probabilistic COVID-19 forecasts in the U.S.” 2020. International Institute of Forecasters Blog.
- Evan L Ray, Nutcha Wattanachit, Jarad Niemi, Abdul Hannan Kanji, Katie House, Estee Y Cramer, Johannes Bracher, Andrew Zheng, Teresa K Yamana, Xinyue Xiong, Spencer Woody, Yuanjia Wang, Lily Wang, Robert L Walraven, Vishal Tomar, Katherine Sherratt, Daniel Sheldon, Robert C Reiner, B. Aditya Prakash, Dave Osthus, Michael Lingzhi Li, Elizabeth C Lee, Ugur Koyluoglu, Pinar Keskinocak, Youyang Gu, Quanquan Gu, Glover E George, Guido España, Sabrina Corsetti, Jagpreet Chhatwal, Sean Cavany, Hannah Biegel, Michal Ben-Nun, Jo Walker, Rachel Slayton, Velma Lopez, Matthew Biggerstaff, Michael A Johansson, Nicholas G Reich, COVID-19 Forecast Hub Consortium. “Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.” 2020. medRxiv. (preprint)
- Bracher J, Ray EL, Gneiting T, Reich NG. “Evaluating epidemic forecasts in an interval format.” 2020. arXiv. (preprint)
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.
- Reich NG, McGowan CJ, Yamana TK, Tushar A, Ray E, Osthus D, Kandula S, Brooks LC, Crawford-Crudell W, Gibson GC, Moore E, Silva R, Biggerstaff M, Johansson MA, Rosenfeld R, Shaman J. “Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.” PLOS Comp Bio. 2019. 15(11): e1007486.
- Reich NG, Brooks L, Spencer F, Kandula S, McGowan C, Moore E, Osthus D, Ray E, Tushar A, Yamana T, Biggerstaff M, Johansson MA, Rosenfeld R, and Shaman J. “A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States.” PNAS. 2019. 116(8): 3146-3154.
- McAndrew T, Reich NG. “Adaptively stacking ensembles for influenza forecasting with incomplete data.” arXiv. 2019. (preprint)
- McGowan C, Biggerstaff M, Johansson M, Apfeldorf K, Ben-Nun M, Brooks L, Convertino M, Erraguntla M, Farrow D, Freeze J, Ghosh S, Hyun S, Kandula S, Lega J, Liu Y, Michaud N, Morita H, Niemi J, Ramakrishnan N, Ray EL, Reich NG, Riley P, Shaman J, Tibshirani R, Vespignani A, Zhang Q, Reed C. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016.” Sci Rep. 2019. 9(683).
- Ray EL, Reich NG. Prediction of infectious disease epidemics via weighted density ensembles.”” PLOS Comp Bio. 2018. 14(2): e1005910.