Veronica J. Berrocal is an Associate Professor of Biostatistics. She received her PhD in Statistics from the University of Washington in 2007, working on developing spatial statistical models for probabilistic weather forecasting. Following her graduation, Veronica was a National Research Council (NRC) Postdoctoral Associate at the US Environmental Protection Agency (EPA) and a postdoctoral research associate at the Department of Statistical Sciences at Duke University and SAMSI (Statistical and Applied Mathematical Sciences Instittute). Veronica's research focuses on developing and applying statistical models for data collected over space and/or over time, with a particular focus on developing methods to infer upon environmental and social determinants of health (e.g. air pollution, weather, built environment, poverty, etc.), characterize environmental exposure and estimate the impact of socio-economic context and environmental risk factors on health. Her main applications are related to: atmospheric sciences, environmental health, environmental epidemiology, rheumatology, reproductive endocrinology, and also some image data.
Veronica is actively involved in the protection of public health from environmental risk factors through her research and also through her participation in Scientific Advisory Panels for the US EPA.
- BIOSTAT696: Spatial Statistics Syllabus (PDF
- BIOSTAT682: Applied Bayesian Inference
- BIOSTAT698: Modern Statistical Methods in Epidemiologic Studies
- BIOSTAT896: Spatial Statistics Syllabus (PDF)
- BIOSTAT501: Online course for HMP Executive Master
- PhD , Statistics, University of Washington, 2007
- M.Sc. , Statistics, Michigan State University, 2002
Research Interests & Projects
Veronica's main research interests are in the development and application of statistical methods for data that exhibit some form of dependence, in particular spatial/spatio-temporal data, and longitudinal data.
She has developed spatio-temporal hierarchical models for the prediction and spatial interpolation of social and environmental determinants of health (e.g. ozone, PM2.5, traffic-related pollutants, poverty, etc.); she has developed statistical models to calibrate deterministic geophysical models (weather, regional climate models), and is currently involved in a project leveraging complex data sources, including social media, to characterize environmental exposure and neighborhood contextual risk factors.
She has investigated geographical variation in health-related outcomes (e.g. veterinary parasites prevalence, Dengue-carrying mosquitoes), as well as spatial variations in the health effects of environmental risk factors.
In terms of medical applications, she has studied and developed methods to characterize the progression of rheumatological rare diseases, as well the change over time of reproductive health in under-represented minorities.
- M.D. Risser, C.A. Calder, V.J. Berrocal, and C. Berrett (2019). Nonstationary spatial prediction of soil organic carbon: Implications for stock assessment decision making. Annals of Applied Statistics. In press.
- Y.-H. Chen, B. Mukherjee, and V.J. Berrocal (2019). Distributed lag interaction models with two pollutants. Journal of the Royal Statistical Society Series C 68, 79-97.
- Z. Liu, A. Bartsch, V.J. Berrocal, and T.D. Johnson (2018). A mixed-effects, spatially-varying coefficient model with application to multi-resolution fMRI data. Statistical Methods in Medical Research. DOI: 10.1177/0962280217752378.
- Y.-H. Chen, B. Mukherjee, S.D. Adar, V.J. Berrocal, and B.A. Coull (2018). Robust distributed lag models using data adaptive shrinkage. Biostatistics 19, 461-478.
- O. Gilani, V.J. Berrocal, and S. Batterman (2016). Predicting traffic-related pollutant concentrations in near-road urban environments using a Bayesian spatio-temporal model. Spatial and spatio-temporal epidemiology 18, 24037.
- L. Cecconi, A. Biggeri, L. Grisotto, V.J. Berrocal, L. Rinaldi, E. Musella, G. Cringoli, and D. Catelan (2016). Informative sampling for veterinary parasitological surveillance: examples for sheep farms in the Campania region. Geospatial Health 11, 62-69
- Z. Liu, V.J. Berrocal, A.J. Bartsch, and T.D. Johnson (2016). Pre-surgical fMRI data analysis using a spatially adaptive conditional autoregressive model. Bayesian Analysis 11, 599-625.
- V.J. Berrocal (2016). Identifying trends in the spatial errors of a regional climate model via clustering. Environmetrics 27, 90-102.