ASA Connect

Overview

Join us for a dynamic and thought-provoking virtual summit exploring how data science is transforming our understanding of environmental impacts on public health. This interdisciplinary conversation will feature a panel of invited experts who will each share brief opening remarks on emerging challenges, opportunities, and ideas at the intersection of data science, environmental science, and public health. A moderated group discussion will follow, aimed at identifying key themes and questions to help shape the October in-person Ideas Lab Workshop.

Featured Panelists

• Jonathan Hobbs – Jet Propulsion Laboratory (JPL)
  Specializing in Earth observation and environmental data systems, Jonathan will share how satellite data is being used to monitor environmental health indicators and inform public health strategies.

• Chris Wikle – University of Missouri
  A pioneer in spatio-temporal modeling, Chris will discuss statistical approaches to understanding complex environmental systems and their interactions with human health.

• Marianthi-Anna Kioumourtzoglou – Columbia University
  An expert in environmental epidemiology, Marianthi-Anna will explore how data science is helping uncover links between pollution exposure and health outcomes across populations.

Key Discussion Points

• Urgent challenges at the intersection of public health and environmental crises where data science can drive meaningful impact over the next decade

• Emerging methodological innovations in statistical modeling, machine learning, and data collection that could transform research at this intersection

• Role of causal inference methods in translating environmental exposure data into actionable policy insights

• Use of mathematical models to detect and anticipate joint tipping points in environmental and human health systems

• Strategies for integrating and harmonizing diverse datasets, including epidemiological registries, environmental monitoring, and biodiversity surveys

• Applications of species abundance and diversity models to inform ecosystem and public health decision-making

• Co-modeling approaches to capture cascading effects of extreme events such as wildfires, droughts, and epidemics

• Balancing innovation in data sources-like satellite imagery, wearable sensors, and community-level reporting-with concerns around data quality, privacy, and representativeness

• Adapting sampling strategies to maintain robust data under increasing uncertainty due to climate change

• Structuring transdisciplinary collaboration among statisticians, environmental scientists, and public health experts to address complex challenges

About the Data Science at the Intersection of Public Health and the Environment - Ideas Lab (Workshop) | National Institute of Statistical Sciences

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.

See full details on event page: Data Science at the Intersection of Public Health and the Environment - Ideas Lab (Workshop) | National Institute of Statistical Sciences

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.