Data Science at the Intersection of Public Health and the Environment - Ideas Lab (Workshop)

Overview

IMSI and the National Institute of Statistical Sciences (NISS) are organizing a workshop on Data Science at the Intersection of Public Health and the Environment. This event will bring together experts from diverse fields to explore innovative methodologies, foster collaboration, and address pressing challenges in public and environmental health using data science techniques.

Scientific Description

Human-natural systems are increasingly interconnected, with data-driven science and engineering enhancing our understanding of these complex interactions. The intersection of environment and public health is of paramount importance, as environmental changes can significantly impact human health outcomes. Statistical and mathematical tools play a crucial role in understanding these intricate relationships, and emergent data sources combined with modern methods offer promising solutions. However, transdisciplinary methodological innovations are necessary to fully tackle these multifaceted challenges.

Key Research Areas:

• New inferential approaches: Summarizing, linking, and analyzing diverse datasets from epidemiological studies, health registries, environmental monitoring, and surveys to uncover shared patterns, trends, associations, and causal relationships.
• Species abundance and diversity modeling: Leveraging big data to assess ecosystem and biodiversity health across different spatial and temporal resolutions.
• Innovative sampling techniques: Designing efficient and representative data collection methods while quantifying variability, bias, and uncertainty in joint environmental and health studies.
• Co-modeling of extremes: Developing methodologies to model the probability and magnitude of rare events in both environmental and human health domains (e.g., floods, wildfires, droughts, pandemics, food insecurity).
• Mathematical modeling of environmental systems: Simulating biological, physical, and chemical processes, hypothesizing tipping points, and integrating causal models to assess intervention impacts.

Workshop Format

This workshop will feature an intensive in-person research studio or ideas lab, fostering high-impact interdisciplinary collaboration. The event will commence with keynote presentations and tutorial sessions, followed by iterative brainstorming and working group formation.

Structure:

1. Virtual Pre-Workshop Events: Introductory tutorials and dataset overviews from leading experts.
2. In-Person Workshop (4-5 days):
   • Keynote presentations on grand challenges and cutting-edge methodologies.
   • Facilitated small-group discussions alternating with full-group presentations and feedback sessions.
   • Development of concrete research questions, collaborative writing, and initial project ideation.
3. Post-Workshop Activities:
   • Follow-up meetings for research working groups.
   • Themed virtual events (hackathons, data visualization sessions, poster sessions for new researchers).
   • Potential joint IMSI-NISS long-term research programs.

Organizing Committee

• Bo Li (Washington University in St. Louis, Statistics & Data Science)
• Simone Gray (CDC/NCCDPHP/DCPC)
• Corwin Zigler (Brown University, Biostatistics)
• David S. Matteson (Cornell University & NISS)
• Dorit Hammerling (Colorado School of Mines, Applied Mathematics & Statistics)
• Mevin Hooten (University of Texas at Austin, Statistics & Data Science)

Proposed Speakers

(This list is still being finalized)

Participation & Abstract Submission

We invite researchers from academia, government, and industry with expertise in statistics, epidemiology, environmental science, and related fields to apply.

How to Join:

• Eligibility: Open to all researchers, with emphasis on interdisciplinary collaborations.
• Application Process: Submit a brief statement of interest and a CV.
• Selection Criteria: Participants will be selected based on their research alignment, expertise, and potential contribution to collaborative efforts.

Abstract Submission:

Participants interested in presenting research or contributing ideas should submit a 300-word abstract summarizing:

• Research focus and relevance to workshop themes.
• Key methodologies employed.
• Potential collaborative research questions.
• Submission Deadline: [To Be Announced]

Logistics

• Workshop Length: 5 days
• Dates: 1 week (October 20-24)
• Venue: IMSI at the University of Chicago.

Additional Activities

The workshop will be complemented by a series of themed semester events, including:

• Virtual research updates from working groups.
• Poster sessions for early-career researchers.
• Hackathons and data visualization challenges.
• Collaborative capstone projects with government and industry partners.

Funding & Support

NISS will provide administrative support for the program, including organization of virtual pre- and post-workshop events. Additional funding may be sought through NISS affiliates to support new researcher travel.

Contact Information

For inquiries, please contact: mglenn@niss.org

Join us in tackling the pressing challenges at the intersection of public health and the environment through data science!