T. Kypraios | Searcharxiv

arXiv2022

Comparative Judgement Modeling to Map Forced Marriage at Local Levels

Forcing someone into marriage against their will is a violation of their human rights. In 2021, the county of Nottinghamshire, UK, launched a strategy to tackle forced marriage and violence against women and girls. However, accessing information about where victims are located in the county could compromise their safety, so it is not possible to develop interventions for different areas of the county. Comparative judgement studies offer a way to map the risk of human rights abuses without collecting data that could compromise victim safety. Current methods require studies to have a large number of participants, so we develop a comparative judgement model that provides a more flexible spatial modelling structure and a mechanism to schedule comparisons more effectively. The methods reduce the data collection burden on participants and make a comparative judgement study feasible with a small number of participants. Underpinning these methods is a latent variable representation that improves on the scalability of previous comparative judgement models. We use these methods to map the risk of forced marriage across Nottinghamshire thereby supporting the county's strategy for tackling violence against women and girls.

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arXiv2021

A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands

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 methodology to fit spatial stochastic transmission models in which the infection rate between any two farms is a function that depends on the distance between them, but without assuming a specified parametric form. Making nonparametric inference in this context is challenging since the likelihood function of the observed data is intractable because the underlying transmission process is unobserved. We adopt a fully Bayesian approach by assigning a transformed Gaussian Process prior distribution to the infection rate function, and then develop an efficient data augmentation Markov Chain Monte Carlo algorithm to perform Bayesian inference. We use the posterior predictive distribution to simulate the effect of different disease control methods and their economic impact. We analyse a large outbreak of Avian Influenza in the Netherlands and infer the between-farm infection rate, as well as the unknown infection status of farms which were pre-emptively culled. We use our results to analyse ring-culling strategies, and conclude that although effective, ring-culling has limited impact in high density areas.

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