Samarth Swarup, University of Virginia, USA
COVID-19 has continued to spread rapidly in populations around the world for the past several months. In the absence of viable pharmaceutical interventions, the only effective strategy for stemming the epidemic has been behavioral modification, such as mask wearing, physical distancing, and social distancing. Enforcement of, and compliance with, these interventions has been uneven, making it difficult to understand their efficacy or to forecast the future spread of the disease. In this talk I will discuss how high resolution mobility data can be used for COVID-19 forecasting, as well as how we are building models of normative reasoning behavior for large-scale simulations. There are numerous technical challenges in this work, including scale, realism, and complexity. I will present ongoing work and discuss paths forward for this urgent domain of research.
Samarth Swarup is a Research Associate Professor in the Biocomplexity Institute at the University of Virginia. His research focuses on the development and analysis of agent-based social simulations, including the use of AI and machine learning-based methods for simulation analytics. His work has been honored with multiple best paper awards. He earned his PhD in Computer Science from the University of Illinois at Urbana-Champaign.