On Thursday, August 21, 2025, the National Institute of Statistical Sciences (NISS) hosted a Virtual Thought Summit bringing together leading experts to discuss how data science is transforming our understanding of environmental impacts on public health. The event served as a prelude to the upcoming IMSI+NISS Ideas Lab Workshop on Data Science at the Intersection of Public Health and the Environment, to be held in Chicago, October 20–24, 2025.
The summit featured a panel of invited experts, each offering insights into emerging challenges and opportunities at the nexus of environmental science, data science, and public health:
- Jonathan Hobbs (Jet Propulsion Laboratory) – highlighted how satellite-based Earth observation systems are being leveraged to monitor environmental health indicators and inform strategies for protecting population health, noting upcoming NASA missions such as Maya and TEMPO.
- Chris Wikle (University of Missouri) – shared advances in spatio-temporal modeling to capture the complexity of environmental systems, their interactions with human health, and the importance of linking extreme events to causal inference.
- Marianthi-Anna Kioumourtzoglou (Brown University) – explored data-driven methods in environmental epidemiology, integrating heterogeneous data sources, machine learning, and statistical innovations to uncover links between pollution exposure and population-level health outcomes.
The discussion was moderated by Bo Li, Stanley A. Sawyer Professor in the Department of Statistics and Data Science and Co-director of the Transdisciplinary Institute in Applied Data Sciences at Washington University in St. Louis.
Opening Insights and Emerging Challenges
Following introductory remarks, panelists underscored the role of satellite data in environmental health monitoring, machine learning for predicting air pollution exposure, and advanced statistical models for capturing the spatio-temporal variability of health and climate data. They emphasized the importance of uncertainty quantification, noting its relevance in areas such as wildfire prediction, air quality alerts, and real-time health risk assessments.
Panelists also discussed data integration challenges, particularly the difficulty of linking diverse data sources—satellite imagery, surveys, registries, and environmental monitoring systems—while protecting privacy and addressing gaps in temporal and spatial resolution. The concept of Digital Twins was highlighted as a promising tool to fuse mechanistic models with real-world data for applications ranging from wildfire response to urban heatwave preparedness.
Interdisciplinary Collaboration and Policy Relevance
The conversation highlighted the challenges of interdisciplinary teamwork, where terminology and methodological differences across statistics, computer science, epidemiology, and environmental engineering often create communication barriers. Panelists stressed the importance of building shared understanding while ensuring research outputs remain relevant to policy and public health practice.
Marianthi-Anna noted the complexities of translating research into regulatory action, particularly in the current political climate, and encouraged community-driven approaches while maintaining consistency with broader scientific trends.
Key Themes Emerging from the Discussion
- The power of Earth observation and remote sensing to provide scalable, high-resolution environmental health indicators.
- The importance of spatio-temporal statistical models for linking environmental variability with human health outcomes.
- Strengthening environmental epidemiology through interdisciplinary collaboration.
- Balancing methodological innovation with policy relevance to ensure impact on decision-making.
- The need for new frameworks to co-model rare and extreme events (wildfires, floods, pandemics) across human and environmental systems.
- Growing urgency to address the “data science without data” paradox, where methodological innovation outpaces robust data collection and sharing.
Recommendations and Next Steps
The panel identified several opportunities for advancing the field:
- Integrate diverse datasets (satellite, health registries, environmental monitoring, surveys) to build comprehensive environment-health linkages.
- Develop new inferential frameworks to strengthen causal analysis in complex, multi-level systems.
- Advance co-modeling of extremes to improve preparedness for climate-related and public health crises.
- Promote innovative sampling strategies to reduce bias, enhance representativeness, and address data gaps.
- Encourage stronger interdisciplinary collaboration to align statistical methods with real-world applications in public health and policy.
- Invest in uncertainty quantification and digital twin technologies to strengthen predictive capacity and resilience.
- Expand funding opportunities beyond NIH, leveraging foundations and international initiatives, while addressing disparities in low- and middle-income contexts.
Connecting to the IMSI+NISS Ideas Lab Workshop (October 20–24, 2025)
The August Thought Summit served as a launchpad for the upcoming IMSI+NISS Ideas Lab Workshop in Chicago, which will gather experts from across disciplines to develop collaborative research initiatives in five priority areas:
- New inferential approaches for linking and analyzing diverse datasets.
- Species abundance and diversity modeling for assessing ecosystem and biodiversity health.
- Innovative sampling techniques to quantify variability, bias, and uncertainty.
- Co-modeling of extremes in environmental and human health domains.
- Mathematical modeling of environmental systems to identify tipping points and intervention strategies.
Together, the Thought Summit and the October Ideas Lab underscore NISS’s mission to advance data-driven insights at the intersection of science, health, and society, positioning data science as a vital tool for addressing the most pressing environmental and public health challenges of our time.
NISS and IMSI extends its sincere gratitude to our moderator, Bo Li (Washington University in St. Louis), and to our panelists—Jonathan Hobbs (JPL), Chris Wikle (University of Missouri), and Marianthi-Anna Kioumourtzoglou (Brown University)—for sharing their expertise and sparking a thoughtful discussion at the Virtual Thought Summit. We also thank the IMSI team and the broader NISS community for their continued collaboration in shaping this important dialogue at the intersection of data science, environmental science, and public health.