UC Berkeley Statisticians use Galileo to estimate COVID-19 fatality rates more accurately, improve data collection

by | Jul 30, 2021 | Biomedical

COVID-19 infections are on the rise again in the US. It’s now as important as ever for policymakers to improve their knowledge of the spread of the disease, its severity, and the ways in which we can optimize resource allocation. UC Berkeley computer science PhD candidate and Galileo user Anastasios Angelopoulos recently led an effort to improve mathematical modeling and data collection to generate better estimates of the relative case fatality ratios for COVID-19 across varying populations.

The other researchers involved in the study are Michael I. Jordan of UC Berkeley (named by Science as the most influential computer scientist alive in 2016), Reese Pathak of UC Berkeley, and Rohit Varma of the Southern California Eye Institute.

Read about the study, results, and Galileo in VentureBeat.
Lead author Angelopoulos turned to Galileo to run the statistical models for this project so that he could concentrate on the extremely time sensitive work at hand and avoid losing time and energy thinking about computing infrastructure:


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