Climate change is changing when and where people are exposed to air pollution, wildfire smoke, heat, and other risks. For applied health researchers and policy partners, the challenge is not only to estimate an average association, but to understand which exposures matter, when they matter, through what pathways, and which communities or sub-populations are most at risk. This is difficult because climate and environmental health data are high-dimensional, spatially and temporally misaligned, uncertain, and linked across cohort, administrative, biomarker, and exposure data systems. This panel brings together expert statistical and environmental exposure modellers in Canada to share methodological and applied advances motivated by climate health questions. The talks will cover multipollutant distributed lag nonlinear models for PM2.5, O3, NO2 and mortality in Ontario; monotone exposure-response modelling for short-term air pollution risk; Bayesian sensitivity analysis for spatial causal inference when individual-level exposure is unobserved; and a multipollutant framework for wildfire smoke PM2.5 composition, biomarkers, and stroke risk.
The presentations will be followed by an interactive discussion on how interdisciplinary input can shape method development, improve knowledge mobilization, and support precision environmental health at neighbourhood and population levels. This is designed as an exchange with applied health, policy, systems, sustainability, and environmental researchers, not a methods-only session. The goal is to identify research opportunities grounded in real climate health problems and useful for decision-making on adaptation, preparedness, and policy.
Moderator
Bo Li, PhD
Bo Li’s research interests lie in spatial and spatiotemporal statistics, with a broad interdisciplinary focus that spans climatology, public health, ecology, agriculture, and forestry. Her research has been supported by the NSF, NIH, NASA, and Sandia National Laboratories. Bo’s work is primarily rooted in environmental statistics. Her contributions to statistical theory and methodology include univariate and multivariate spatiotemporal data modeling, functional data analysis, multiple testing, copula methods, Bayesian hierarchical modeling, and spatial extremes.
Panelists
Meredith Franklin, PhD, MSc
Meredith Franklin is a Professor in the Department of Statistical Sciences and holds a joint appointment with the School of the Environment. Her interdisciplinary research focuses on quantifying environmental exposures through spatiotemporal statistical and machine learning approaches to assess their impacts on health outcomes. She has been a leader in developing methods to use remote sensing data for exposure assessment for a variety of environmental factors.
Guowen Huang, PhD
Guowen Huang is an Assistant Professor in the Department of Statistical and Actuarial Sciences at Western University. His main research interest focuses on statistical modeling of air pollution data and quantitatively analyzing its impact on human health. He received his PhD degree in Statistics (2016) from the University of Glasgow under the supervision of professors Duncan Lee and Marian Scott.
Kuan Liu, PhD, MMath
Kuan Liu is a Tier II Canada Research Chair in Causal Methods for Health Research and an Assistant Professor at the Institute of Health Policy, Management & Evaluation at the University of Toronto. Her work targets the development of novel statistical methodologies motivated by applications in public health research.
Glen McGee, PhD
Glen McGee is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He completed his PhD in Biostatistics at Harvard University and previously received a BScH in Mathematics from Queen’s University. He is interested in developing statistical tools to solve problems in epidemiology, environmental health, and health policy.