Publications

Published papers (reverse chronological order)

  1. Swallow, B., Rand, D.A. and Minas, G., (2024) ‘Bayesian inference for stochastic oscillatory systems using the phase-corrected Linear Noise Approximation’ accepted in Bayesian Analysis
  2. Alexander, R., et al. (2024) ‘Target Trial Emulation: Impact of C-Section on Birth Outcomes for Spontaneous Preterm Breech Presentations’, International Journal of Population Data Science, 9(5). doi: 10.23889/ijpds.v9i5.2785.
  3. Rudan, I, et al,. (2024) ‘Selecting the most informative positive and negative controls for self-controlled case series (SCCS): Rationale, approach, and lessons from studies investigating the safety of COVID-19 vaccines’, J Glob Health 14:03037.
  4. Macdonald, C. et al., (2024) ‘Association between antibody responses post-vaccination and severe COVID-19 outcomes: national population-based cohort study in Scotland’, NPJ Vaccines, 9, 107
  5. Summers, R.W., Swallow, B., Fridman, J., Hokkanen, T., Newton, I. & Buckland, S.T. (2024) ‘Irruptions of crossbills Loxia spp. in northern Europe – patterns and correlations with seed production by key and non-key conifers’, Ibis in press
  6. Millington, T. et al. (2024) Caveats in Reporting of National Vaccine Uptake (accepted in Journal of Global Health)
  7. Shi, T. et al. (2023) Risk of winter hospitalisation and death from acute respiratory infections in Scotland, in press Journal of the Royal Society of Medicine
  8. Swallow, B., (2023) Ben Swallow’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker, (in press JRSSB)
  9. Jun Villejo, S., Illian, J. and Swallow, B., (2023) ‘Data Fusion in a Two-stage Spatio-Temporal Model using the INLA-SPDE Approach.’, (in press Spatial Statistics)
  10. Firat, E., Swallow, B. and Laramee, R.S., (2023) ‘PCP-Ed: Parallel coordinate plots for ensemble data’, (accepted in Visual Informatics)
  11. Rydow, E. et al., (2022) ‘Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modelling’ (accepted for publication in IEEE TVCG)
  12. Shadbolt, N. et al., (2022) ‘The Challenges of Data in Future Pandemics’ (accepted for publication in special issue of Epidemics)
  13. Marion, G. et al., ‘Modelling: understanding pandemics and how to control them’ (accepted for publication in special issue of Epidemics)
  14. Zhang, H., Swallow, B. and Gupta, M., (2022) ‘Bayesian hierarchical mixture models for detecting non-normal clusters applied to noisy genomic and environmental datasets’, (accepted for invited special issue, Aust. N.Z. J. Stat)
  15. Panovska-Griffiths, J. et al., (2022) ‘Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions’, accepted for themed issue of PTRSA
  16. Dunne, M. et al., (2022) ‘Complex model calibration through emulation, a worked example for a stochastic epidemic model’ (accepted for publication in special issue of Epidemics)
  17. Chadwick, F. et al., (2022) ‘Combining Rapid Antigen Testing and Syndromic Surveillance Improves Sensitivity and Specificity of COVID-19 Detection: A Community-Based Prospective Diagnostic Study.’, accepted for publication in Nature Comms.
  18. Swallow, B., Xiang, W. and Panovska-Griffiths, J., (2022) ‘Tracking the national and regional COVID-19 epidemic status in the UK using directed Principal Component Analysis.’, accepted for themed issue of PTRSA
  19. Dykes, J. et al., (2022) ‘Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations’, accepted for themed issue of PTRSA
  20. Swallow, B., et al., (2022) ‘Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling’, (in press in Epidemics, https://doi.org/10.1016/j.epidem.2022.100547)
  21. Kretzschmar, M. et al., (2022) ‘Challenges for modelling interventions for future pandemics’ (in press in Epidemics, https://doi.org/10.1016/j.epidem.2022.100546)
  22. Hadley, L. et al., (2021) ‘Challenges on the interaction of models and policy for pandemic control,’ (in press in Epidemics, https://doi.org/10.1016/j.epidem.2021.100499)
  23. Sacchi, G. and Swallow, B. (2021) ‘Parallel tempering as a mechanism for facilitating inference in hierarchical hidden Markov models.’, (in press in Frontiers in Ecology and Evolution)
  24. Swallow, B., Buckland, S. T., King, R. and Toms, M. P. (2019) Assessing factors associated with changes in the numbers of birds visiting gardens in winter: are predators partly to blame?’ (Ecology and Evolution, 9, 12182– 12192) and journal blog post
  25. Jones-Todd, C. M., Swallow, B., Illian, J. B. and Toms, M. P. (2018) A spatio-temporal multi-species model of a semi-continuous response.’ (JRSS(C), 67(3), 705–722)
  26. Swallow, B., King, R., Buckland, S. T. and Toms, M. P. (2016) Identifying multi-species synchrony in response to environmental covariates.’ (Ecology and Evolution, 6(23), 8515–8525)
  27. Swallow, B., Buckland, S. T., King, R. and Toms, M. P. (2015) ‘Bayesian Hierarchical Modelling of Continuous Non-negative Longitudinal Data with a Spike at Zero: An Application to a Study of Birds Visiting Gardens in Winter.’ (Biometrical Journal Special Issue, 58(2), 357–371, Special) and press release from BTO.

Peer reviewed reports

  1. Data Study Group team. (2020). Data Study Group Final Report: Roche. Zenodo.

Book reviews

  1. Swallow, B., (2021) A review of Applied Hierarchical Modeling in Ecology: Volume 2 by Kéry and Royle, (JABES, https://doi.org/10.1007/s13253-021-00440-8)

Preprints/under submission

  1. Gunn, E., Sengupta, N. and Swallow, B., (2024) Gaussian process modelling of infectious diseases using the Greta software package and GPUs, arXiv:2411.05556
  2. Li, X., Swallow, B. and Chadwick, F.J. ‘A Novel Approximate Bayesian Inference Method for Compartmental Models in Epidemiology using Stan’
  3. Stroud, J., (2024) ‘Fast Gaussian Processes for Bayesian inference - How smooth is your latent process?’
  4. Swallow, B., Grier, J. and Panovska-Griffiths, J., (2024) ‘Spatio-temporal transmissibility and dispersion of SARS-CoV-2 variants and sub-variants of concern in England’
  5. Hu, C., Swallow, B. and Castro-Camilo, D., (2024) ‘A Bayesian multivariate extreme value mixture model’ arXiv:2401.15703
  6. Gould, E., et al., (2024) ‘Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology’, EcoEvoArxiv and Nature news item
  7. Xiang, W. and Swallow, B. (2021) ‘Multivariate spatio-temporal analysis of the global COVID-19 pandemic.’
  8. Swallow, B., Rigby, M., Rougier, J.C., Manning, A.J., Lunt, M. and O’Doherty S. (2017) ‘Parametric uncertainty in complex environmental models: a cheap emulation approach for models with high-dimensional output applied to greenhouse gas emissions estimation.’

Journals I have reviewed for

  • Bayesian Analysis
  • JRSS(C)
  • Journal of
  • Annals of Applied Statistics
  • Biometrics
  • Journal of Theoretical Biology
  • Forrest Ecology and Management
  • Studies in Automation and Information Technology
  • Chemometrics and Intelligent Laboratory Systems (awarded Outstanding Reviewer status November 2016)
  • Proceedings of the Royal Society A
  • Biology Letters
  • JABES
  • Environmental and Ecological Statistics
  • Natural Resource Modeling
  • Statistical Methods in Medical Research