Predicting pandemic death rates: UMass Amherst biostatistician oversees the COVID-19 Forecast Hub that creates critical weekly reports at the state and U.S. level
As the U.S. struggles to contain COVID-19 and understand the impact of reopening businesses and schools, University of Massachusetts Amherst biostatistician Nicholas Reich is carrying out groundbreaking pandemic forecasting work with the COVID-19 Forecast Hub.
Reich's weekly forecasts of deaths from the coronavirus across the U.S. and by state, looking four weeks ahead, are used by the U.S. Centers for Disease Control to predict the trajectory of the pandemic. Reich, who also heads the CDC-designated UMass Influenza Forecasting Center of Excellence, follows the same ensemble approach for COVID-19, unifying multiple models from top forecasters and institutions around the world. The hub features a centralized, open-science data repository available to federal and state agencies, data journalists and the public at large.
Reich also produces and posts on the hub a weekly report that puts the latest predictions into perspective.
“Our work continues to underscore the importance of looking at multiple different infectious disease models, just as weather forecasters do with hurricane projections, if we want to have a good sense of what is coming next with COVID-19.” Reich quote may be used by media.
“As we move forward into a very uncertain phase of the COVID-19 outbreak, it is vital that we be critical consumers of models. By looking at models from different research groups, we can improve our understanding of the range of possible future outcomes." Reich quote may be used by media.
Dr. Reich is available to speak to media about forecasting trends and the death toll from COVID-19 in the U.S. and different states. Simply click on his icon to arrange an interview.
Nicholas Reich Professor of Biostatistics / Director of COVID-19 Forecast Hub / Director of Influenza Forecasting Center of Excellence
Nicholas Reich's research focuses on infectious disease modeling and optimizing design and analysis for cluster-randomized studies.