Welcome to the Network Systems Science and Advanced Computing (NSSAC) collaboration page. We are a division of the Biocomplexity Institute (BII) at the University of Virginia. We use advanced modeling techniques and simulations to study real world, cross-discipline problems. Our focus areas include Cognitive and Social Behaviors, Interdependent Infrastructures (like transportation and social media), Systems Biology, and Public Health (including epidemics and pandemics). This site provides access to materials relevant to current topics of interest in order to foster communication and collaboration. For additonal information about our research, please see our main webpage.
Check out our world class speakers on our YouTube channels: NSSAC, Global Pervasive Computational Epidemiology, PREPARE, Net.Science
We are proud to support the recently completed U.K.-U.S. prize challenges.
In the challenge, participants were tasked with creating personalized risk forecasts of infection in a privacy-preserving manner. The challenge was put on by the U.K.’s Center for Data Ethics and Innovation (CDEI) and Innovate UK, as well as by the U.S. National Institutes of Standards and Technology (NIST), and the National Science Foundation (NSF) in cooperation with the White House Office of Science and Technology Policy (OSTP).
Our team generated the two synthetic population datasets provided in the pandemic response challenge. The first covers the population of the UK, and the second the population of the state of Virginia, USA. We used an outbreak simulation that created 63 days-worth of data, subsequently split into 56 days of training data and 7 days of test data. You can learn more about our contributions here and access the dataset here.
Presented to Jiangzhuo Chen, Bryan Lewis, and Srini Venkatramanan as representatives of the Biocomplexity Institute’s COVID-19 Response Team for their service to the University, the Commonwealth of Virginia, and federal authorities during the pandemic, which continues today. Link
The team was recognized at SC21 for their paper describing an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. Link
The team was selected as a finalist in the June 2021 Trinity Challenge for better protecting the world against health emergencies using data-driven research and analytics. 16 finalists were chosen out of 340 entries. Link
Presented to A Bhatele, J Yeom, N Jain, C Kuhlman, Y Livnat, K Bisset, L Kale, M Marathe for innovative use of HPC that led to scalable mapping of epidemic simulations on NERSC machines. Link
Presented to the team in the category of Data to Decisions for work on developing high performance computing solutions to support national disaster management. Link
Presented to the team for our work on simulating the entire US population in seconds versus an hour, helping to contain influenza outbreaks and optimizing placement of treatment centers for the Ebola outbreak. Link