BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UCLA//iCal Event Generator//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:ca8cecb7-c412-4460-9201-09084813ff81
DTSTAMP:20230322T042205Z
DTSTART:20200716T190000Z
DTEND:20200716T200000Z
SUMMARY:Combining Traditional Modeling with Machine Learning for Predicting COVID-19
URL:https://healthpolicy.ucla.edu/newsroom/events/seminar/combining-traditional-modeling-machine-learning-predicting-covid-19?language=tl
URL;VALUE=URI:https://healthpolicy.ucla.edu/newsroom/events/seminar/combining-traditional-modeling-machine-learning-predicting-covid-19?language=tl
DESCRIPTION:https://healthpolicy.ucla.edu/newsroom/events/seminar/combining-traditional-modeling-machine-learning-predicting-covid-19?language=tl\n\nIn her July 16 seminar, Christina Ramirez, a professor of Biostatistics at the UCLA Fielding School of Public Health, shared her groundbreaking, comprehensive model that combined traditional SEIR models with case velocity and machine learning to get precise, reliable estimates of COVID-19 case and death rates — shining a light on whether the pandemic is gaining speed and if deaths are accelerating or stabilizing.

LOCATION:Online
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