Bayesian Statistical Modeling of Spatiotemporal Datasets at Small-Area Levels

Please join us for an engaging presentation by Dr. Hui Luan, who will discuss recent advances in Bayesian statistical modeling applied to spatiotemporal datasets, with a focus on small-area health and environmental data. His work integrates spatial statistics and GIS to better understand disparities in health outcomes and access to care. All are welcome to attend.

Abstract: The increasing availability of geographical health datasets at small-area levels (e.g., census tracts, zip codes) provides unprecedented opportunities to uncover the underlying spatiotemporal processes of health phenomena at fine scales. Statistical modeling of these datasets, however, is challenged with various issues including data censoring and zero-inflation, which are usually intertwined with the well-known challenges of spatial/temporal autocorrelation and space-time interaction. Using censored and zero-inflated HIV diagnosis and pre-exposure prophylaxis datasets, this talk illustrates how the Bayesian approach, a framework that integrates both observed data and prior information for statistical inferences, can flexibly address these issues while simultaneously quantifying inference uncertainties.

Speaker: Hui Luan, Ph.D.Faculty Profile – UT Southwestern

Dr. Hui (Henry) Luan is an Assistant Professor in Spatial Data Science and Spatial Epidemiology in the O’Donnell School of Public Health at UT Southwestern Medical Center. He received his PhD degree in Planning from University of Waterloo, Canada, and M.S. and B.S. degrees in GIS from Wuhan University, China. The goal of Dr. Luan's research is to advance Bayesian probabilistic inferences in geospatial health data analysis, promote the application of GIS and spatial analysis in public health, inform the development of geographically tailored, evidence-based health intervention programs, and ultimately improve population health. His recently funded NIH R01 project leverages extensive social determinants data and spatial data science to help reduce HIV incidence in the United States.

Zoom meeting:
https://harvard.zoom.us/j/92593096112?pwd=L9XF4N5hEcUtSs0NKcLbvhUybJNhfv.1

Password: CGA2025