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dc.contributor.authorYu, Bin
dc.date.accessioned2020-11-04T21:33:54Z
dc.date.available2020-11-04T21:33:54Z
dc.date.issued2020-10-23
dc.identifier.urihttp://hdl.handle.net/1853/63809
dc.descriptionPresented online on October 23, 2020 at 2:00 p.m.en_US
dc.descriptionBin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley and a former chair of Statistics at UC Berkeley. Yu's research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine.en_US
dc.descriptionRuntime: 58:26 minutesen_US
dc.description.abstractAs the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead. Using data from January 22 to June 20, 2020, we develop and combine multiple forecasts using ensembling techniques, resulting in an ensemble we refer to as Combined Linear and Exponential Predictors (CLEP). Our individual predictors include county-specific exponential and linear predictors, a shared exponential predictor that pools data together across counties, an expanded shared exponential predictor that uses data from neighboring counties, and a demographics-based shared exponential predictor. We use prediction errors from the past five days to assess the uncertainty of our death predictions, resulting in generally-applicable prediction intervals, Maximum (absolute) Error Prediction Intervals (MEPI). MEPI achieves a coverage rate of more than 94% when averaged across counties for predicting cumulative recorded death counts two weeks in the future. Our forecasts are currently being used by the non-profit organization, Response4Life, to determine the medical supply need for individual hospitals and have directly contributed to the distribution of medical supplies across the country. We hope that our forecasts and data repository at this https URL can help guide necessary county-specific decision-making and help counties prepare for their continued fight against COVID-19.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesIDEaS-AI Seminar Seriesen_US
dc.relation.ispartofseriesTRIAD Distinguished Lecture Seriesen_US
dc.subjectCOVID-19en_US
dc.subjectCurationen_US
dc.subjectDataen_US
dc.subjectForecastingen_US
dc.titleCurating a COVID-19 data repository and forecasting county-level death counts in the United States​en_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Data Engineering and Scienceen_US
dc.contributor.corporatenameUniversity of California, Berkeley. Dept. of Electrical Engineering and Computer Sciencesen_US
dc.contributor.corporatenameUniversity of California, Berkeley. Dept. of Statisticsen_US


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