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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160013Z
LOCATION:Track 10
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UID:submissions.supercomputing.org_SC20_sess388_cov104@linklings.com
SUMMARY:A Population Data-Driven Workflow for COVID-19 Modeling and Learni
 ng
DESCRIPTION:ACM Gordon Bell COVID Finalist, Awards Presentation\n\nA Popul
 ation Data-Driven Workflow for COVID-19 Modeling and Learning\n\nOzik, Woz
 niak, Collier, Macal, Binois\n\nCityCOVID is a detailed agent-based model 
 (ABM) that represents the behaviors and social interactions of 2.7 million
  residents of Chicago as they move between and colocate in 1.2 million dis
 tinct places, including households, schools, workplaces and hospitals, as 
 determined by individual hourly activity schedules and dynamic behaviors s
 uch as isolating because of symptom onset. Disease progression dynamics in
 corporated within each agent track transitions between possible COVID-19 d
 isease states, based on heterogeneous agent attributes, exposure through c
 olocation, and effects of self-protective behaviors on viral transmissibil
 ity. Throughout the COVID-19 epidemic, CityCOVID model outputs have been p
 rovided to city, county and state stakeholders in response to evolving dec
 ision-making priorities, incorporating emerging information on SARS-CoV-2 
 epidemiology. Here we demonstrate our efforts in integrating our high-perf
 ormance epidemiological simulation model with large-scale machine learning
  to develop a generalizable, flexible and performant analytical platform f
 or planning and crisis response.\n\nRegistration Category: Tech Program Re
 g Pass
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