spatio-temporal modeling

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

Xiang Chen, Ph.D. Student, Statistics

Apr 30, 13:00 - 16:00

B5 R5209

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Fernando Rodriguez Avellaneda

Ph.D. Student, Statistics

spatio-temporal modeling environmental research Spatial epidemiology Python (Programming Language) data science

Fernando Rodriguez Avellaneda's research focuses on Bayesian spatial and spatio-temporal modeling, with applications in environmental and epidemiological studies.