Qiangqiang Zhu
Email: q.zhu.1@research.gla.systa-s.com
https://orcid.org/0009-0002-4377-6880
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 Statistics
- Environmental Epidemiology
Publications
2026
Zhu, Qiangqiang, Lee, Duncan 
ORCID: https://orcid.org/0000-0002-6175-6800 and Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306
(2026)
Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown.
Statistical Methods in Medical Research,
(Accepted for Publication)
2024
Zhu, Qiangqiang, Lee, Duncan ORCID: https://orcid.org/0000-0002-6175-6800 and Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306
(2024)
A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland.
Environmental and Ecological Statistics, 31,
pp. 1085-1108.
(doi: 10.1007/s10651-024-00635-5)
Articles
Zhu, Qiangqiang, Lee, Duncan ORCID: https://orcid.org/0000-0002-6175-6800 and Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306
(2026)
Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown.
Statistical Methods in Medical Research,
(Accepted for Publication)
Zhu, Qiangqiang, Lee, Duncan ORCID: https://orcid.org/0000-0002-6175-6800 and Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306
(2024)
A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland.
Environmental and Ecological Statistics, 31,
pp. 1085-1108.
(doi: 10.1007/s10651-024-00635-5)
Supervisors
Conferences
Additional information
- MSc in Statistics at University of Southampton, 2021-2022
- BSc in Applied Statistics at Shanghai University of International Business and Economics, 2017-2021