All Outputs (10)

Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models (2022)
Journal Article

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

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

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

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

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

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

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

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 non-parametric inference for stochastic epidemic models using Gaussian Processes (2016)
Journal Article

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarel... Read More about Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

Bayesian model choice for epidemic models with two levels of mixing (2013)
Journal Article

This paper considers the problem of choosing between competing models for infectious disease final outcome data in a population that is partitioned into households. The epidemic models are stochastic individual-based transmission models of the suscep... Read More about Bayesian model choice for epidemic models with two levels of mixing.