Published papers (reverse chronological order)

  1. 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)
  2. Shadbolt, N. et al., (2022) ‘The Challenges of Data in Future Pandemics’ (accepted for publication in special issue of Epidemics)
  3. Marion, G. et al., ‘Modelling: understanding pandemics and how to control them’ (accepted for publication in special issue of Epidemics)
  4. Zhang, H., Swallow, B. and Gupta, M., ‘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)
  5. Panovska-Griffiths, J. et al., (2022) ‘Modelling transmissibility and impact on reopening Roadmap of different SARS-CoV-2 variants in England in the Spring of 2021’, accepted for themed issue of PTRSA
  6. 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)
  7. 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.
  8. 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
  9. Dykes, J. et al., (2022) ‘Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations’, (accepted for themed issue of PTRSA)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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
  15. 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)
  16. 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)
  17. 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

  1. Jun Villejo, S., Illian, J. and Swallow, B., ‘Data Fusion in a Two-stage Spatio-Temporal Model using the INLA-SPDE Approach.
  2. Swallow, B., Rand, D.A. and Minas, G. (2022), ‘Bayesian inference for stochastic oscillatory systems using the phase-corrected Linear Noise Approximation.
  3. Xiang, W. and Swallow, B. (2021) ‘Multivariate spatio-temporal analysis of the global COVID-19 pandemic.
  4. 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.

Under subimission

  1. Firat, E., Swallow, B. and Laramee, R.S., ‘Techniques for Dense Parallel Coordinate Plots’, (submitted to EuroVis 2022)

Journals I have reviewed for