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BEGIN:VEVENT
DTSTAMP:20220812T074334Z
LOCATION:Samarkand Room
DTSTART;TZID=Europe/Stockholm:20220627T170000
DTEND;TZID=Europe/Stockholm:20220627T173000
UID:submissions.pasc-conference.org_PASC22_sess136_msa159@linklings.com
SUMMARY:Integrating Simulation, Machine Learning, and High-Performance Com
 puting to Support Public Health Decision Making
DESCRIPTION:Minisymposium\n\nIntegrating Simulation, Machine Learning, and
  High-Performance Computing to Support Public Health Decision Making\n\nOz
 ik\n\nThe COVID-19 pandemic has highlighted the need for detailed modeling
  approaches that can capture the many complexities of emerging infectious 
 diseases. In response, our group developed CityCOVID, a distributed agent-
 based model capable of tracking COVID-19 transmission in large, urban area
 s. Through partnerships between Argonne National Laboratory, the Universit
 y of Chicago, the Chicago Department of Public Health, and the Illinois CO
 VID-19 Modeling Task Force we combined multiple data sources to develop a 
 locally informed, realistic, and statistically representative synthetic ag
 ent population, with attributes and processes that reflect real-world soci
 al and biomedical aspects of transmission. We model all 2.7 million indivi
 dual residents of Chicago, as they go to and from 1.2 million different pl
 aces according to their individual hourly schedules. In this presentation 
 I will describe how we integrated agent-based modeling, machine learning, 
 and high-performance computing (HPC) technologies in support of public hea
 lth stakeholders. I will describe our efforts in translating the outputs o
 f our HPC-generated analyses to support public health decision making in u
 nderstanding, responding to and planning for the current and future popula
 tion health emergencies.\n\nDomain: Climate, Weather and Earth Sciences, H
 umanities and Social Sciences, Life Sciences
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