Qiangqiang Zhu
Email: q.zhu.1@research.gla.ac.uk
Research title: Spatio-temporal modelling of population-level disease risk when the populations at risk have partially unknown spatial locations
Research Summary
Research interests
- Spatio-temporal prediction for air pollutant concentrations
- Effects of air pollution on public health
Current research
My current research is investigating and comparing different mothods to predictict air pollutant concentrations for the whole Scotland. Specifically, this study will predict air pollutant concentrations at a high spatio-temporal resolution by comparing the performance of
- linear models, e.g. normal linear regression models and generalised linear models;
- spatio-temporal models, e.g. Spatial AR models and Kriging;
- machine learing methods, e.g. random forests regression,
using an air quality monitoring dataset and meteorological predictors from multiple locations. The study will then highlight the optimal model with the best predictive performance, which is used to draw air pollutant concentration maps for the whole Scotland in each month of the study period.
Supervisors
Grants
- China Scholarship Council (CSC), 2022-2026
Conference
Teaching
Tutoring work:
- STAT3011/STAT4052 - Data Analysis, 2022-23
- STAT4041/STAT5014 - Bayesian Statistics, 2022-23
- STAT5024 - Probability (Level M), 2022-23
- STAT5025 - Regression Models, 2022-23
- STAT5028 - Statistical Inference (Level M), 2022-23
