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‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys
(Oxford University Press, 2019-08-23) Dawkins L; Williamson DB; Barr S; Lampkin SR
The city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large on‐line survey, and similar individuals have been grouped to explore distinct behaviour profiles to inform intervention design to reduce commuter congestion. Statistical analysis within societal applications benefit from incorporating available social scientist expert knowledge. Current clustering approaches for the analysis of social surveys assume that the number of groups and the within‐group narratives are unknown a priori. Here, however, informed by valuable expert knowledge, we develop a novel Bayesian approach for creating a clear opposing transport mode group narrative within survey respondents, simplifying communication with project partners and the general public. Our methodology establishes groups characterizing opposing behaviours based on a key multinomial survey question by constraining parts of our prior judgement within a Bayesian finite mixture model. Drivers of group membership and within‐group behavioural differences are modelled hierarchically by using further information from the survey. In applying the methodology we demonstrate how it can be used to understand the key drivers of opposing behaviours in any wider application.
Prevalence and genetic diversity of Theileria equi from horses in Xinjiang Uygur Autonomous region, China.
(Elsevier B.V., 2023-07-01) Zhang Y; Shi Q; Laven R; Li C; He W; Zheng H; Liu S; Lu M; Yang DA; Guo Q; Chahan B
Theileria equi is a tick-borne intracellular apicomplexan protozoan parasite that causes equine theileriosis (ET). ET is an economically important disease with a worldwide distribution that significantly impacts international horse movement. Horses are an essential part of the economy in Xinjiang which is home to ∼10% of all the horses in China. However, there is very limited information on the prevalence and genetic complexity of T. equi in this region. Blood samples from 302 horses were collected from May to September 2021 in Ili, Xinjiang, and subjected to PCR examination for the presence of T. equi. In addition, a Bayesian latent class model was employed to estimate the true prevalence of T. equi, and a phylogenetic analysis was carried out based on the 18S rRNA gene of T. equi isolates. Seventy-two horses (23.8%) were PCR positive. After accounting for the imperfect PCR test using a Bayesian latent class model, the estimated true prevalence differed considerably between age groups, being 10.8% (95%CrI: 5.8% - 17.9%) in ≤ 3-year-old horses and 35.7% (95%CrI: 28.1% - 44.5%) in horses that were > 3 year-old. All T. equi isolates had their 18S rRNA gene (430bp) sequenced and analyzed in order to identify whether there were multiple genotypes of T. equi in the Xinjiang horse population. All of the 18S rRNA genes clustered into one phylogenetic group, clade E, which is thus probably the dominant genotype of T. equi in Xinjiang, China. To summarize, we monitored the prevalence of T. equi in horses of Xinjiang, China, with a focus on the association between age and the occurrence of T. equi by Bayesian modelling, accompanied by the genotyping of T. equi isolates. Obtaining the information on genotypes and age structure is significant in monitoring the spread of T. equi and studying the factors responsible for the distribution.
Bayesian models of age and growth in sharks : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Information Sciences, School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
(Massey University, 2023) Bristow, James
Marine ecosystems are under increasing pressure from factors such as overfishing, that have lead to reported declines in chondrichthyan populations across the globe. Approximately 80% of New Zealand chondrichthyian species have no specific management or monitoring, and a lack of species-specific information has led to uncertainty concerning population trends over time. Biological parameters such as growth rates are typically incorporated into fisheries growth models, the most common of which is the von Bertalanffy growth model (VBGM). The common VBGM is monophasic, and is therefore a continuous and monotone curve over all stages of growth. The VBGM has received some criticism concerning the assumption of monophasic growth. One critique is that a single curve fails to account for changes in energy reallocation and growth that occur during the transition between the juvenile and mature life stages of the shark. Conversely, a biphasic growth model may better model the patterns of both juvenile and mature sharks. The biphasic VBGM (BVBGM) has offered promising results when applied to chondrichthyans in previous growth studies. Estimating the ages of chondrichthyans is typically performed by trained readers who count the growth bands deposited within the vertebral centra. However, the reading of vertebrae is subjective, error-prone, and time consuming. While vertebral band pairs are the most commonly used structure for age estimation, there are various sources of bias and uncertainty inherent to the reading process. Deep learning models such as convolutional neural networks (CNNs) have demonstrated promise for the automated interpretation of bony fish otolith growth zones, though the application of CNNs for the automated reading of chondrichthyan vertebrae has yet to be explored. The principal goal of this thesis is to advance methodologies for estimating the growth parameters of sharks via the application of Bayesian models, thereby offering more robust management and conservation of chondrichthyes against various factors such as overfishing. We first explored the potential of biphasic growth models on five species of New Zealand chondrichthyes: Centrophorus squamosus; Isurus oxyrinchus; Lamna nasus; Mustelus lenticulatus; and Prionace glauca. We compared two monophasic and two biphasic growth models using the Pareto-smoothed importance sampling approximation of leave-one-out cross-validation (PSIS-LOO) metric. Biphasic models appeared to provide superior fit for both males and females in the majority of cases, and we were able to improve upon prior examples from the literature where parameter estimates were noted to be biased or poor. Our overall results demonstrated that the popular monophasic VBGM should not be chosen a priori as the only candidate model to describe the growth of chondrichthyans. Instead, we should consider multiple alternative growth models, as informed by statistical evidence and domain expertise. We next explored the feasibility of automating the age estimation of chondrichthyans by training CNNs on an image dataset of Isurus oxyrinchus vertebrae. We evaluated three Bayesian deep learning methods for uncertainty quantification: DeepEnsembling, mixture of Laplace approximations (MoLA), and multi-stochastic weight average Gaussian (MultiSWAG) in terms of predictive power and model calibration. We found that MultiSWAG offered marginally superior predictive performance and model calibration relative to DeepEnsembling and MoLA. Moreover, predictions produced by MultiSWAG typically closely matched the estimates provided by human readers, though the performance of these deep learning models tended to degrade for older age classes. We argue that our results demonstrate promise for emulating trained readers, leading to potential efficiency gains and cost savings. However, we note that there is a lack of evidence that our models are directly counting the bands of the vertebrae, and that further refinement of our CNNs may be required. Our findings have demonstrated the ability of Bayesian methods to perform principled uncertainty quantification and parameter estimation within the context of age and growth modelling. Our Bayesian growth models were able to quantify the epistemic uncertainty of our parameter estimates, offering more robust estimation of growth parameters and superior model fit. Additionally, we were able to incorporate prior information into our growth models, as informed by the available literature and domain expertise. Likewise, the application of Bayesian deep learning facilitated the quantification of epistemic and aleotoric uncertainty for our age estimates, while also offering superior predictive performance and well-calibrated prediction intervals. We showed that Bayesian CNNs could be used to efficiently automate the interpretation of vertebral growth bands for the purposes of age estimation. This study has contributed to the research of New Zealand sharks, marine conservation, and fisheries management by improving methods used to measure and interpret growth parameters. We hope it contributes to the management and persistence of these species for future generations.

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