Workshops

These workshops convened public health decision-makers, modelers, and researchers to envision a future with improved pandemic response, demonstrate means for co-producing research plans to pursue that vision, and inform the design of specific information products using Robust Decision Making methods that could support robust public health decisions in such a future. At the start of this project, we conducted a backcasting exercise with our public health partners, asking them to imagine that it is 2035 and that their agency has successfully managed a pandemic. We also asked them to describe the information and decision-support tools that helped them achieve this happy result. The project team extracted from these stories a list of research priorities. We then conducted a decision-framing workshop focusing on a subset of these priorities, a specific pandemic response decision, and the information our partners would want to address the challenge. The project team then conducted a pilot analysis based on this decision framing and presented proof-of-concept visualizations of the results. We then gathered stakeholders’ opinions about the perceived utility of the analysis and discussed future research agendas.

For more information please visit the workshop summary report.

The NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project successfully convened a workshop entitled, “One Health surveillance and predictive intelligence for eco-epidemiological modeling” at the University of Chicago on April 27-28. 2023. This was part of a series of workshops designed to foster sustainable and interdisciplinary co-design for predictive intelligence and pandemic prevention. The event gathered 35 experts in eco-epidemiological surveillance, along with public health stakeholders, to build a shared vision for the required data and sensing capabilities to improve computational epidemiology. The workshop adopted the CDC’s One Health framework, a comprehensive, transdisciplinary approach that recognizes the interconnectedness of human, animal, plant, and environmental health at all levels. The workshop participants delved into key areas where data collected from multiple sources—air, water, animals, soils, and plants—could be optimized to enhance predictive intelligence for holistic epidemiological modeling. This report documents the key findings and takeaways provided by the participants during the workshop. This report documents the key findings and takeaways from the workshop.

In person at UChicago, May 1-2, 2023

 The NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project successfully convened a workshop entitled “High-performance computing and large-scale data management in service of epidemiological modeling” at the University of Chicago on May 1-2, 2023. This was part of a series of workshops designed to foster sustainable and interdisciplinary co-design for predictive intelligence and pandemic prevention. The event brought together 31 experts in epidemiological modeling, high-performance computing (HPC), HPC workflows, and large-scale data management to develop a shared vision for capabilities needed for computational epidemiology to better support pandemic prevention. Through the workshop, participants identified key areas in which HPC capabilities could be used to improve epidemiological modeling, particularly in supporting public health decision-making, with an emphasis on HPC workflows, data integration, and HPC access. The workshop explored nascent HPC workflow and large-scale data management approaches currently in use for epidemiological modeling and sought to draw from approaches used in other domains to determine which practices could be best adapted for use in epidemiological modeling. This report documents the key findings and takeaways from the workshop.