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Webinar | Argonne National Laboratory

Arctic Master Works Webinar

Part of the Arctic Master Works webinar series which highlights the impact of advanced computing in health sciences, energy, and environmental research. Organized by Argonne, NREL, University of Iceland, and Reykjavik University.

Webinar Schedule: Two 30-minute talks, 30-minute panel with Q&A

Seminar 1: Impact of U.S. Vaccination Strategy on COVID-19 Wave Dynamics” by Anna Sigríður Islind and María Óskarsdóttir, Reykjavík University

Abstract: We employ the epidemic Renormalization Group (eRG) framework to understand, reproduce, and predict the COVID-19 pandemic diffusion across the United States. The human mobility across different geographical divisions is modeled via open-source flight data alongside the impact of social distancing for each such division. We analyze the impact of the vaccination strategy on the current pandemic wave dynamics in the United States. We observe that the ongoing vaccination campaign will not impact the current pandemic wave and therefore, strict social distancing measures must still be enacted. To curb the current and the next waves, our results indisputably show that vaccinations alone are not enough, and strict social distancing measures are required until sufficient immunity is achieved. Our results are essential for a successful vaccination strategy in the United States.

Bio: Anna Sigríður Islind is an assistant professor in the Department of Computer Science at Reykjavik University. She holds a doctorate in informatics from University West in Sweden.

Bio: María Óskarsdóttir is an assistant professor in the Department of Computer Science at Reykjavík University. She holds a doctorate in business analytics from KU Leuven, Belgium, and a master’s degree in mathematics from the Leibniz University Hannover, Germany.

Seminar 2: Agent-based Modeling of COVID-19 to Support Public Health Decision Making” by Jonathan Ozik (DIS)

Abstract: The COVID-19 pandemic has highlighted the need for detailed modeling approaches that can capture the myriad complexities of emerging infectious diseases. In response, our group has developed CityCOVID, an 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. The places include locations such as households, workplaces, schools, and hospitals, and, as individuals congregate with other individuals in these places over the course of their daily routines, they are exposed to potential infection from other infectious people who are also at those places. Transitions between disease states depend on agent attributes and exposure to infected individuals, placed-based risks, and protective behaviors.

This detailed modeling approach allows us to implement very specific and realistic mitigation strategies that are being considered by stakeholders, and which have been evolving over the course of the pandemic. We continue to apply CityCOVID to examine the impact of non-pharmaceutical interventions, SARS-CoV-2 variants of concern, vaccination deployment strategies, and to understand the impacts of social determinants of health on disease outcomes. In this presentation I will describe CityCOVID, including how the synthetic population was developed, what agent-based modeling and high-performance computing technologies were required, and our efforts in supporting local public health stakeholders in understanding, responding to and planning for the current and future population health emergencies.