Data Analytics for the Environment and Population Health

We develop new Bayesian and spatial statistical methods that are motivated by emerging data streams and research questions in environmental and population health research. 

We conduct population-based epidemiologic studies using large health databases (e.g., birth registry, hospital discharge, electronic medical records and disease surveillance system). 

We develop spatial-temporal statistical models and machine learning algorithms to estimate environmental exposures by integrating multiple data sources (e.g., satellite imagery, model simulations and low-cost sensors). 

We develop approaches to address under-counting of mortality and morbidity outcomes attributable to respiratory infections at the local, regional and global scales.

We provide support in data acquisition, harmonization and management. We also assist with analysis of longitudinal, spatial and time-to-event data for risk factor identification and predictive modeling. 

Hsiao TW, Gaskins AJ, Warren JL, Darrow LA, Strickland MJ, Russell AG, Chang HH

Huang XF, Iuliano AD, Ebelt S, Reed C, Chang HH

McCann Z, Zhang H, Mueller K, Qian Y, Lane M, Dsouza R, Weil EJ, Ebelt S, Scovronick N, Chang HH.

Madden WG, Qi M, Liu Y, Chang HH

Englert JR, Ebelt ST, Chang HH