A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

Nicholas G Reich, Logan Brooks, Spencer J. Fox, +11

Proceedings of the National Academy of Sciences

Forecasting MethodologyStrain SelectionComputational EpidemiologyUnited StatesReal-time Forecasts +19

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Estee Y. Cramer, Evan L Ray, Velma K. Lopez, +289

Proceedings of the National Academy of Sciences

Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.

Nicholas G Reich, Craig McGowan, Teresa K. Yamana, +13

PLoS Computational Biology

Forecasting MethodologyEngineeringMachine LearningEnsemble MethodsInfluenza Vaccines +19

Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

Evan L Ray, Nutcha Wattanachit, Jarad Niemi, +35

Forecasting MethodologyVirus EpidemiologyCovid-19 EpidemiologyCollaborative EnsembleCovid-19 +17

Forecasting seasonal influenza with a state-space SIR model

Dave Osthus, Kyle S. Hickmann, Petruţa C. Caragea, +2

The Annals of Applied Statistics

The United States COVID-19 Forecast Hub dataset

Estee Y. Cramer, Yuxin Huang, Yijin Wang, +421

Scientific Data

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Katharine Sherratt, Hugo Gruson, Rok Grah, +125

eLife

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Estee Y. Cramer, Evan L Ray, Velma K. Lopez, +289

Population Health SciencesComputational EpidemiologyCovid-19 EpidemiologyUnited StatesPublic Health Action +22

Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)

Dave Osthus, James Gattiker, Reid Priedhorsky, +1

Bayesian Analysis

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Karen M. Holcomb, Sarabeth M. Mathis, J. Erin Staples, +27

Parasites & Vectors