Dr Oliver Stoner

  • Lecturer (Statistics)

Biography

I am a newly appointed Lecturer in Statistics. Prior to joining, I was a Research Fellow for 2 years at the University of Exeter, where I also obtained my PhD in 2019. My thesis is entitled Bayesian Hierarchical Modelling Frameworks for Flawed Data in Environment and Health.

Research interests

I am always motivated to advance statistical modelling within other disciplines and I'm currently contributing to projects spanning epidemiology, ecology, environmental hazards, and clinical sciences.

The two main projects I am leading are an impact-oriented collaboration with the World Health Organization and a more methodologically-driven research programme in correcting delayed reporting of infectious diseases.

Household Air Pollution

Around 2.6 billion people mainly use polluting fuels for cooking, hampering their socio-economic prospects and exposing them to dangerous levels of household air pollution, attributed by the World Health Organization to 3.8 million deaths per year in 2016. I have worked with the WHO to develop a bespoke Bayesian hierarchical model for estimating the populations using 6 different fuels for cooking, called the Global Household Energy Model. Estimates from GHEM serve as the primary basis for tracking access to clean cooking as part of the UN Sustainable Development Goals, and are a key input to WHO burden of disease calculations for household air pollution.

Our new paper presenting these estimates in all their glory has been accepted in principle by Nature Communications. I have also contributed as a named author to the 2020 and 2021 editions of the internationally recognised report Tracking SDG7: The Energy Progress Report.

Correcting Delayed Reporting of Infectious Diseases

Surveillance of infectious disease cases and/or deaths is often subject to delayed reporting, where complete information is not immediately available to decision-makers. Statistical models can learn the systematic and random structures in the reporting delay, to predict current case/death counts based on incomplete data. 

The best introduction to this work I can offer is an article we prepared for Significance Magazine early on in the COVID-19 pandemic A more transparent way to estimate and report daily Covid-19 deaths, aimed at a general statistical audience.

During my PhD we developed a new multivariate hierarchical framework for correcting delayed reporting. This was published in Biometrics just before COVID-19 started appearing in our news feeds, and as a postdoc I sought to demonstrate this method as a powerful operational tool for pandemic decision-making.

Our work on delayed reporting followed our work on a new method for correcting under-reporting, published in the Journal of the American Statistical Association in 2019.

 

 

Publications

List by: Type | Date

Jump to: 2021 | 2020 | 2019
Number of items: 6.

2021

Stoner, O. , Lewis, J., Martínez, I. L., Gumy, S., Economou, T. and Adair-Rohani, H. (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, O. and Economou, T. (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, O. and Economou, T. (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, O. , Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (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, O. , Economou, T. and Drummond Marques da Silva, G. (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, L. S., Economou, T., Gomes, M. F.C., Villela, D. A.M., Coelho, F. C., Cruz, O. G., Stoner, O. , Bailey, T. and Codeço, C. 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)

This list was generated on Tue Nov 30 08:31:39 2021 GMT.
Jump to: Articles
Number of items: 6.

Articles

Stoner, O. , Lewis, J., Martínez, I. L., Gumy, S., Economou, T. and Adair-Rohani, H. (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, O. and Economou, T. (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, O. and Economou, T. (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, O. , Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (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, O. , Economou, T. and Drummond Marques da Silva, G. (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, L. S., Economou, T., Gomes, M. F.C., Villela, D. A.M., Coelho, F. C., Cruz, O. G., Stoner, O. , Bailey, T. and Codeço, C. 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)

This list was generated on Tue Nov 30 08:31:39 2021 GMT.

Grants

I have not yet had any grant awards at Glasgow.

At Exeter, I was awarded the following grants to further my research goals:

 

IIB Open Innovation Platform: Collaboration Fund (2021, CoI)

Funding to develop advanced prediction models for harmful algal blooms and impacts on the shellfish industry. Paper submitted to Nature Food in June 2021.

 

ESRC Impact Accelerator Sub-Award: Knowledge Exchange Fellowship (2021, PI)

Funding to support a collaborative paper with the WHO on modelling polluting household energy.

 

I have also been PI on two research contracts between the WHO and the University of Exeter, to support annual updates to Sustainable Development Goal 7 and to contribute to associated reporting.

 

 

Supervision

I currently have the following projects available:

  • Advanced methods for simulating rainfall time series at multiple locations, to inform environmental risk assessment.
  • New methods modelling compositional time series data.

Prospective students are advised to contact me by email (oliver.stoner@glasgow.ac.uk) to discuss project ideas and opportunities for fully-funded studentships.

I am currently supervising PhD student Alba Halliday, who is working on developing operational models for spatio-temporal disease prediction.

Teaching

In the 2021/22 academic year, I will be teaching Advanced Data Analysis and supervising 8 fourth-year statistics projects.