geospatial statistics

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

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B5 R5209

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

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B4/5 L0 A0215; Zoom Meeting 94713495879

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.

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

Xiang Chen

Ph.D. Student, Statistics
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.

Hanan Alahmadi

Ph.D. Student, Statistics

Elias Teixeira Krainski

Research Scientist, Statistics
Elias' research focuses on efficient Bayesian methods for practical implementation, spatio and spatio-temporal statistics, INLA.

Damilya Saduakhas

Ph.D. Student, Statistics
Damilya Saduakhas is a Ph.D. candidate in the KAUST Stochastic Processes and Mathematical Statistics Research group (StochProc) under the supervision of Professor David Bolin. Before joining KAUST, Damilya obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Research Interests Damilya's research interests include geospatial statistics, multivariate spatial processes, environmental modeling, and applications.