Dr Oliver Stoner
- Senior Research Fellow (Statistics)
email:
Oliver.Stoner@glasgow.ac.uk
Mathematics and Statistics Building, 132 University Place, Glasgow City, Scotland, United Kingdom, G12 8TA
https://orcid.org/0000-0003-0612-4306Biography
Oliver Stoner is a Senior Research Fellow in the School of Mathematics and Statistics. His research focuses on the development and application of statistical methodology for sustainable development, public health and environmental science, with particular expertise in Bayesian hierarchical modelling.
His main area of specialism is developing statistical methods for monitoring sustainable development goals and challenges, including methods used for official international estimation and reporting. His research has been applied to World Health Organization and United Nations Sustainable Development Goal monitoring, global assessments of household energy use and air pollution, and infectious-disease surveillance systems.
Oliver has been a member of international expert groups, including the World Health Organization expert group on modelling household air-pollution exposure and the Food and Agriculture Organization expert working group on modelling wood fuel.
Oliver joined the University of Glasgow in 2021 following a Postdoctoral Research Fellowship at the University of Exeter. He completed his PhD in Mathematics at Exeter in 2019, where he developed Bayesian hierarchical modelling frameworks for flawed data.
He received the Royal Statistical Society David Cox Research Prize in 2023 and in 2024 led the project team awarded the Glasgow Changing Futures Award at the University of Glasgow Knowledge and Innovation Awards.
Research interests
Statistical methods for monitoring sustainable development
Oliver's main area of work is developing statistical methods for monitoring sustainable development goals and challenges, especially where reliable national or global estimates are required from sparse, heterogeneous or incomplete data.
This work includes the development of hierarchical modelling approaches that borrow information across countries, regions, time periods and related outcomes while appropriately quantifying uncertainty. These methods have been applied in official international estimation and reporting for the United Nations Sustainable Development Goals and in related global monitoring programmes.
His work in this area includes long-standing collaborations with the World Health Organization and the Food and Agriculture Organization of the United Nations, spanning household energy, air pollution, wood-fuel and forest-product statistics, and food-loss estimation. He has contributed to several WHO, FAO, and multi-agency policy reports.
Household energy and air pollution
A major application of this work is the statistical modelling of global household energy use.
Oliver developed the Global Household Energy Model, a multivariate Bayesian hierarchical framework for estimating national trends in household cooking-fuel use. The resulting modelling framework has formed the basis for estimation of Sustainable Development Goal indicator 7.1.2 since 2018 and contributes to international monitoring of access to clean fuels and technologies for cooking.
These country-level fuel estimates were presented in Nature Communications and contribute to international reporting including Tracking SDG7: The Energy Progress Report.
This research has developed into a continuing programme with the World Health Organization's work on household air pollution. Current work extends the modelling to provide a more complete picture of household energy use, notably producing the first estimates of global reliance on polluting fuels and technologies for household space heating, with the aim of improving the monitoring of energy transitions and their implications for health and sustainable development.
Disease surveillance and nowcasting
Another strand of Oliver's research concerns statistical methods for correcting incomplete disease-surveillance data.
His doctoral research developed Bayesian hierarchical frameworks for correcting under-reporting and delayed reporting in count data. This included a multivariate hierarchical framework for delayed reporting, published in Biometrics, and a hierarchical framework for correcting under-reporting, published in the Journal of the American Statistical Association.
The delayed-reporting methodology was subsequently extended during the COVID-19 pandemic, including in work on correcting delayed reporting of COVID-19. Related methodology was used by researchers at the MRC Biostatistics Unit as part of real-time COVID-19 nowcasting and forecasting work that informed UK pandemic decision-making.
The wider programme has also contributed to operational infectious-disease surveillance in Brazil, including the InfoDengue and InfoGripe systems. More recent research has developed joint Bayesian nowcasting of Severe Acute Respiratory Illness and COVID-19 positives in Brazil, published in Statistics in Medicine.
Research groups
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, 35(6),
pp. 1215-1229.
(doi: 10.1177/09622802261439259)
(PMID:42029144)
(PMCID:PMC13283498)
Halliday, Alba, Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo and Bastos, Leonardo Soares
(2026)
Joint Bayesian nowcasting of severe acute respiratory illness and COVID-19 positives in Brazil.
Statistics in Medicine, 45(8-9),
e70529.
(doi: 10.1002/sim.70529)
(PMID:41998813)
(PMCID:PMC13090138)
2025
Steel, E. A. et al. (2025) Global wood fuel production estimates and implications. Nature Communications, 16(1), 6227. (doi: 10.1038/s41467-025-59733-y) (PMID:40664666) (PMCID:PMC12264136)
Economou, Theo, Parliari, Daphne, Tobias, Aurelio, Dawkins, Laura, Steptoe, Hamish, Sarran, Christophe, Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Lowe, Rachel and Lelieveld, Jos
(2025)
Flexible distributed lag models for count data using mgcv.
American Statistician, 79(3),
pp. 371-382.
(doi: 10.1080/00031305.2025.2505514)
(PMID:40747491)
(PMCID:PMC12312768)
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)
Stoner, O. ORCID: https://orcid.org/0000-0003-0612-4306, Economou, T. and Brown, A.R.
(2024)
Seasonal early warning of impacts of harmful algal blooms on farmed shellfish in coastal waters of Scotland.
Water Resources Research, 60(10),
e2023WR034.
(doi: 10.1029/2023WR034889)
2023
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Halliday, Alba and Economou, Theo
(2023)
Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method.
Biometrics, 79(3),
pp. 2537-2550.
(doi: 10.1111/biom.13810)
(PMID:36484382)
(PMCID:PMC9877609)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo, Torres, Ricardo, Ashton, Ian and Brown, A. Ross
(2023)
Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach.
Harmful Algae, 121,
102363.
(doi: 10.1016/j.hal.2022.102363)
2022
Dawkins, Laura C., Osborne, Joe M., Economou, Theodoros, Darch, Geoff J.C. and Stoner, Oliver R. ORCID: https://orcid.org/0000-0003-0612-4306
(2022)
The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator.
Journal of Hydrology, 607,
127478.
(doi: 10.1016/j.jhydrol.2022.127478)
2021
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Lewis, Jessica, Martínez, Itzel Lucio, Gumy, Sophie, Economou, Theo and Adair-Rohani, Heather
(2021)
Household cooking fuel estimates at global and country level for 1990 to 2030.
Nature Communications, 12,
5793.
(doi: 10.1038/s41467-021-26036-x)
(PMID:34608147)
(PMCID:PMC8490351)
2020
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306 and Economou, Theo
(2020)
An advanced hidden Markov model for hourly rainfall time series.
Computational Statistics and Data Analysis, 152,
107045.
(doi: 10.1016/j.csda.2020.107045)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306 and Economou, Theo
(2020)
Multivariate hierarchical frameworks for modeling delayed reporting in count data.
Biometrics, 76(3),
pp. 789-798.
(doi: 10.1111/biom.13188)
(PMID:31737902)
(PMCID:PMC7540263)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Shaddick, Gavin, Economou, Theo, Gumy, Sophie, Lewis, Jessica, Lucio, Itzel, Ruggeri, Giulia and Adair-Rohani, Heather
(2020)
Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking.
Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(4),
pp. 815-839.
(doi: 10.1111/rssc.12428)
2019
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo and Drummond Marques da Silva, Gabriela
(2019)
A hierarchical framework for correcting under-reporting in count data.
Journal of the American Statistical Association, 114(528),
pp. 1481-1492.
(doi: 10.1080/01621459.2019.1573732)
Bastos, Leonardo S., Economou, Theodoros, Gomes, Marcelo F.C., Villela, Daniel A.M., Coelho, Flavio C., Cruz, Oswaldo G., Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Bailey, Trevor and Codeço, Claudia T.
(2019)
A modelling approach for correcting reporting delays in disease surveillance data.
Statistics in Medicine, 38(22),
pp. 4363-4377.
(doi: 10.1002/sim.8303)
(PMID:31292995)
(PMCID:PMC6900153)
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, 35(6),
pp. 1215-1229.
(doi: 10.1177/09622802261439259)
(PMID:42029144)
(PMCID:PMC13283498)
Halliday, Alba, Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo and Bastos, Leonardo Soares
(2026)
Joint Bayesian nowcasting of severe acute respiratory illness and COVID-19 positives in Brazil.
Statistics in Medicine, 45(8-9),
e70529.
(doi: 10.1002/sim.70529)
(PMID:41998813)
(PMCID:PMC13090138)
Steel, E. A. et al. (2025) Global wood fuel production estimates and implications. Nature Communications, 16(1), 6227. (doi: 10.1038/s41467-025-59733-y) (PMID:40664666) (PMCID:PMC12264136)
Economou, Theo, Parliari, Daphne, Tobias, Aurelio, Dawkins, Laura, Steptoe, Hamish, Sarran, Christophe, Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Lowe, Rachel and Lelieveld, Jos
(2025)
Flexible distributed lag models for count data using mgcv.
American Statistician, 79(3),
pp. 371-382.
(doi: 10.1080/00031305.2025.2505514)
(PMID:40747491)
(PMCID:PMC12312768)
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)
Stoner, O. ORCID: https://orcid.org/0000-0003-0612-4306, Economou, T. and Brown, A.R.
(2024)
Seasonal early warning of impacts of harmful algal blooms on farmed shellfish in coastal waters of Scotland.
Water Resources Research, 60(10),
e2023WR034.
(doi: 10.1029/2023WR034889)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Halliday, Alba and Economou, Theo
(2023)
Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method.
Biometrics, 79(3),
pp. 2537-2550.
(doi: 10.1111/biom.13810)
(PMID:36484382)
(PMCID:PMC9877609)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo, Torres, Ricardo, Ashton, Ian and Brown, A. Ross
(2023)
Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach.
Harmful Algae, 121,
102363.
(doi: 10.1016/j.hal.2022.102363)
Dawkins, Laura C., Osborne, Joe M., Economou, Theodoros, Darch, Geoff J.C. and Stoner, Oliver R. ORCID: https://orcid.org/0000-0003-0612-4306
(2022)
The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator.
Journal of Hydrology, 607,
127478.
(doi: 10.1016/j.jhydrol.2022.127478)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Lewis, Jessica, Martínez, Itzel Lucio, Gumy, Sophie, Economou, Theo and Adair-Rohani, Heather
(2021)
Household cooking fuel estimates at global and country level for 1990 to 2030.
Nature Communications, 12,
5793.
(doi: 10.1038/s41467-021-26036-x)
(PMID:34608147)
(PMCID:PMC8490351)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306 and Economou, Theo
(2020)
An advanced hidden Markov model for hourly rainfall time series.
Computational Statistics and Data Analysis, 152,
107045.
(doi: 10.1016/j.csda.2020.107045)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306 and Economou, Theo
(2020)
Multivariate hierarchical frameworks for modeling delayed reporting in count data.
Biometrics, 76(3),
pp. 789-798.
(doi: 10.1111/biom.13188)
(PMID:31737902)
(PMCID:PMC7540263)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Shaddick, Gavin, Economou, Theo, Gumy, Sophie, Lewis, Jessica, Lucio, Itzel, Ruggeri, Giulia and Adair-Rohani, Heather
(2020)
Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking.
Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(4),
pp. 815-839.
(doi: 10.1111/rssc.12428)
Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Economou, Theo and Drummond Marques da Silva, Gabriela
(2019)
A hierarchical framework for correcting under-reporting in count data.
Journal of the American Statistical Association, 114(528),
pp. 1481-1492.
(doi: 10.1080/01621459.2019.1573732)
Bastos, Leonardo S., Economou, Theodoros, Gomes, Marcelo F.C., Villela, Daniel A.M., Coelho, Flavio C., Cruz, Oswaldo G., Stoner, Oliver ORCID: https://orcid.org/0000-0003-0612-4306, Bailey, Trevor and Codeço, Claudia T.
(2019)
A modelling approach for correcting reporting delays in disease surveillance data.
Statistics in Medicine, 38(22),
pp. 4363-4377.
(doi: 10.1002/sim.8303)
(PMID:31292995)
(PMCID:PMC6900153)
Grants
Oliver has secured funding supporting multi-year research collaboration and secondments with the World Health Organization's Energy and Health team, leading statistical modelling work related to Sustainable Development Goal 7 and household energy and air pollution.
He has also contributed to externally funded interdisciplinary research, including a UK Government Regulators' Pioneer Fund project investigating new methods for monitoring harmful algal blooms affecting shellfish production.
Supervision
Oliver currently supervises Sara Euzzor, who is developing the first time series estimates of the use of different lighting fuel and technology categories at country, regional and global levels over the period 2000–2024, to support policy discussions around energy access and polluting lighting.
Previous supervision has included Qiangqiang Zhu on air-pollution modelling and health effects, Catherine Holland on methods for modelling compositional data with zeros and missing components, and Alba Halliday on Bayesian disease nowcasting, including joint nowcasting of severe acute respiratory illness (SARI) and COVID-positive SARI in Brazil.
