Speaker: Anna Sigríður Islind and María Óskarsdóttir, Reykjavík University
Title: Impact of U.S. Vaccination Strategy on COVID-19 Wave Dynamics
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.
Anna’s 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. She usually works in a multidisciplinary context, and her focus is on the design, development, and use of digital technology in general and on the use of data for health benefits in particular. The digital platform she was a part of developing during her doctorate has now been implemented and used in clinical practice at Sahlgrenska University Hospital in Sweden. The patients gather their own data continuously through wearables, sensors, and mobile apps. The data feeds into a digital platform, and the physicians and nurses use the data as an integrated part of the clinical decision-making process. She is currently supervising two doctoral students working on data-driven health at Reykjavík University and one doctoral student working on data-driven health in Sweden.
Maria’s 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. Her research is focused on practical applications of data science and analytics, whereby she leverages advanced machine learning techniques, network science, and various sources of data with the goal of increasing the impact of the analytics process and facilitating better usg of data science for decision-making, currently focusing on sleep measurements. Óskarsdóttir is the director of the master's program in data science and applied data Science at Reykjavík University. She supervises two doctoral students working on data-driven health.
Speaker: Jonathan Ozik (Computational Scientist @Argonne National Laboratory)
Title: Agent-based Modeling of COVID-19 to Support Public Health Decision Making
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.
Bio: Jonathan is a Computational Scientist at Argonne National Laboratory, Senior Scientist with Public Health Sciences Affiliation in the Consortium for Advanced Science and Engineering at the University of Chicago, and Senior Institute Fellow in the Northwestern Argonne Institute for Science and Engineering. 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.