March 30th, 2023, 11:00 am
The 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 areas. Through partnerships between Argonne National Laboratory, the University of Chicago, the Chicago Department of Public Health, and the Illinois COVID-19 Modeling Task Force we combined multiple data sources to develop a locally informed, realistic, and statistically representative synthetic agent population, with attributes and processes that reflect real-world social and biomedical aspects of transmission. We model all 2.7 million individual residents of Chicago, as they go to and from 1.2 million different places according to their individual hourly schedules. In this presentation I will describe how we integrated agent-based modeling (Repast HPC, ChiSIM), machine learning (IMABC, MOBO), and high-performance computing (HPC) technologies (Swift/T, EMEWS) in support of public health stakeholders. I will describe our efforts in translating the outputs of our HPC-generated analyses to support public health decision making in understanding, responding to and planning for the current and future population health emergencies. I will also describe other areas of application where our integrated approaches have and are continuing to be applied. I will conclude with thoughts on future areas of interest.
Bio:
Jonathan Ozik, Ph.D. – Principal Computational Scientist at Argonne National Laboratory, Senior Scientist in the Consortium for Advanced Science and Engineering at the University of Chicago, and Senior Institute Fellow, Northwestern Argonne Institute of Science and Engineering (NAISE), Northwestern University.
Dr. Ozik develops applications of large-scale agent-based models, including models of infectious diseases, healthcare interventions, biological systems, water use and management, critical materials supply chains, and critical infrastructure. He also applies large-scale model exploration across modeling methods, including agent-based modeling, microsimulation and machine/deep learning. Dr. Ozik is PI of multiple U.S. National Science Foundation and National Institutes of Health projects, and leads the Repast project (repast.github.io) for agent- based modeling toolkits and the Extreme-scale Model Exploration with Swift (EMEWS) framework for large-scale model exploration capabilities on high performance computing resources (emews.org).
