Public Health

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.

Geospatial Data Science for Public Health Surveillance

Paula Moraga, Assistant Professor, Statistics

Mar 9, 11:15 - 12:45

B4/5 L0 A0215; Zoom Meeting 94713495879

Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods

This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.

Geospatial Data Science for Public Health Surveillance

Paula Moraga, Assistant Professor, Statistics

Nov 20, 12:00 - 13:00

B9 L2 R2325

statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis

This talk will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance, highlighting challenges related to data biases and availability.

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.