All Outputs (9)

Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models (2022)
Journal Article
Aristotelous, G., Kypraios, T., & O'Neill, P. D. (2023). Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models. Bayesian Analysis, 18(4), 1283-1310. https://doi.org/10.1214/22-ba1336

We address the problem of assessing the fit of stochastic epidemic models to data. Two novel model assessment methods are developed, based on disease progression curves, namely the distance method and the position-time method. The methods are illustr... Read More about Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models.

Bayesian nonparametric inference for heterogeneously mixing infectious disease models (2022)
Journal Article
Seymour, R. G., Kypraios, T., & O'Neill, P. D. (2022). Bayesian nonparametric inference for heterogeneously mixing infectious disease models. Proceedings of the National Academy of Sciences, 119(10), Article e2118425119. https://doi.org/10.1073/pnas.2118425119

Infectious disease transmissionmodels require assumptions about how the pathogen spreads between individuals. These assumptions may be somewhat arbitrary, particularly when it comes to describing how transmission varies between individuals of differe... Read More about Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands (2021)
Journal Article
Seymour, R. G., Kypraios, T., O’Neill, P. D., & Hagenaars, T. J. (2021). A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands. Journal of the Royal Statistical Society: Series C, 70(5), 1323-1343. https://doi.org/10.1111/rssc.12515

Infectious diseases on farms pose both public and animal health risks, so understanding how they spread between farms is crucial for developing disease control strategies to prevent future outbreaks. We develop novel Bayesian nonparametric methodolog... Read More about A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands.

Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole?genome?sequence data (2020)
Journal Article
Cassidy, R., Kypraios, T., & O'Neill, P. D. (2020). Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole?genome?sequence data. Statistics in Medicine, 39(12), 1746-1765. https://doi.org/10.1002/sim.8510

Whole genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years there have been numerous n... Read More about Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole?genome?sequence data.

Pair-based likelihood approximations for stochastic epidemic models (2019)
Journal Article
Stockdale, J. E., Kypraios, T., & O'Neill, P. D. (2021). Pair-based likelihood approximations for stochastic epidemic models. Biostatistics, 22(3), 575-597. https://doi.org/10.1093/biostatistics/kxz053

Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense... Read More about Pair-based likelihood approximations for stochastic epidemic models.

Bayes Factors for Partially Observed Stochastic Epidemic Models (2018)
Journal Article
Alharthi, M., Kypraios, T., & O'Neill, P. D. (2019). Bayes Factors for Partially Observed Stochastic Epidemic Models. Bayesian Analysis, 14(3), 927-956. https://doi.org/10.1214/18-BA1134

We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literat... Read More about Bayes Factors for Partially Observed Stochastic Epidemic Models.

Bayesian nonparametrics for stochastic epidemic models (2018)
Journal Article
Kypraios, T., & O'Neill, P. D. (2018). Bayesian nonparametrics for stochastic epidemic models. Statistical Science, 33(1), https://doi.org/10.1214/17-STS617

The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approach... Read More about Bayesian nonparametrics for stochastic epidemic models.

Reconstructing transmission trees for communicable diseases using densely sampled genetic data (2016)
Journal Article
Worby, C. J., O'Neill, P. D., Kypraios, T., Robotham, J. V., De Angelis, D., Cartwright, E. J., …Cooper, B. S. (2016). Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10(1), https://doi.org/10.1214/15-AOAS898

Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measur... Read More about Reconstructing transmission trees for communicable diseases using densely sampled genetic data.

Bayesian model choice via mixture distributions with application to epidemics and population process models
Book
O'Neill, P. D., & Kypraios, T. Bayesian model choice via mixture distributions with application to epidemics and population process models. University of Nottingham

We consider Bayesian model choice for the setting where the observed data are partially observed realisations of a stochastic population process. A new method for computing Bayes factors is described which avoids the need to use reversible jump appro... Read More about Bayesian model choice via mixture distributions with application to epidemics and population process models.