A Bayesian spatio-temporal study of meteorological factors affecting the spread of COVID-19

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as the flu it's suggested that COVID-19 may become seasonal as immunity grows. Yet the effects of meteorological conditions on the spread of COVID-19 are poorly understood with previous studies producing contrasting results, due at least in part to limited and inconsistent study designs. This study investigates the effect of meteorological conditions on COVID-19 infections in England using a spatio-temporal model applied to case counts during the initial England lockdown. By modelling spatial and temporal effects to account for the nature of a human transmissible virus the model isolates meteorological effects. Inference based on 95% highest posterior density intervals shows humidity is negatively associated with COVID-19 spread. The lack of evidence for other weather factors affecting COVID-19 transmission shows care should be taken with respect to seasonality when designing COVID-19 policies and public communications.Comment: 23 pages, 13 figures (inclusive of references and appendix

A Bayesian spatio-temporal study of the association between meteorological factors and the spread of COVID-19

BACKGROUND: The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population, transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as influenza some authors have suggested COVID-19 may become seasonal as immunity grows. Despite this, the effects of meteorological conditions on the spread of COVID-19 are poorly understood. Previous studies have produced contrasting results, due in part to limited and inconsistent study designs. METHODS: This study investigates the effects of meteorological conditions on COVID-19 infections in England using a Bayesian conditional auto-regressive spatio-temporal model. Our data consists of daily case counts from local authorities in England during the first lockdown from March-May 2020. During this period, legal restrictions limiting human interaction remained consistent, minimising the impact of changes in human interaction. We introduce a lag from weather conditions to daily cases to accommodate an incubation period and delays in obtaining test results. By modelling spatio-temporal random effects we account for the nature of a human transmissible virus, allowing the model to isolate meteorological effects. RESULTS: Our analysis considers cases across England's 312 local authorities for a 55-day period. We find relative humidity is negatively associated with COVID-19 cases, with a 1% increase in relative humidity corresponding to a reduction in relative risk of 0.2% [95% highest posterior density (HPD): 0.1-0.3%]. However, we find no evidence for temperature, wind speed, precipitation or solar radiation being associated with COVID-19 spread. The inclusion of weekdays highlights systematic under reporting of cases on weekends with between 27.2-43.7% fewer cases reported on Saturdays and 26.3-44.8% fewer cases on Sundays respectively (based on 95% HPDs). CONCLUSION: By applying a Bayesian conditional auto-regressive model to COVID-19 case data we capture the underlying spatio-temporal trends present in the data. This enables us to isolate the main meteorological effects and make robust claims about the association of weather variables to COVID-19 incidence. Overall, we find no strong association between meteorological factors and COVID-19 transmission

Statistical approaches for metabolomics and omics data integration

Biological processes are the result of multiple interactions between various omic entities and are inherently complex. Metabolomics profiling plays a key role into deciphering mechanisms of biological functions in living organisms and is hence gaining popularity. In the last twenty years, the parallel acquisition of high-throughput datasets from the genome, metabolome, proteome, and transcriptome has seen a tremendous boost. The integrative analysis of these datasets is promising to enhance the understanding of biological functions and uncover their underlying mechanisms. The main objectives of this thesis consist in i) developing and investigating novel statistical models for integrative analysis of metabolomics data with other omics technologies, ii) enriching the offer of probabilistic models tailored to metabolomics data and iii) providing enhanced interpretability of results. This thesis is mainly concerned with designing models that can be introduced in different steps of a typical analysis pipeline of metabolomics data. Chapters 2 and 3 motivate the importance of data integration and review popular statistical techniques used in metabolomics. Chapter 4 simultaneously covers two steps of the analysis pipeline, by building a single integrative Bayesian model that is able to perform both cross-omics biomarker discovery and infer potential perturbed pathways. Chapter 5 focuses solely on integrative statistical analysis by uncovering hidden associations between multi-omics data. Finally, in Chapter 6 we investigate the incorporation of pathway information into a Bayesian nonparametric clustering model and its potential to help metabolite annotation. Where possible, simulation studies are used to get a better understanding of our methods and test their applicability. These simulations are always followed by analysis of real data and comparison to competing methods. In most instances, our methods have resulted in plausible biological findings when applied to real data, and represent, to our knowledge, one of the first applications of such probabilistic models in integrative analysis of metabolomics data.Open Acces

Fostering Collaborative Learning Through Postgraduate Group Projects in Statistics Education

Master of Science (MSc) research projects are an important constituent of learning in the postgraduate journey for most curricula. This article reports on the implementation of MSc group work projects for the final postgraduate thesis. To evaluate and measure students' attitude towards this idea, a two-level approach was designed: first a focus group to gauge students’ attitudes and second, a detailed survey incorporating comments from the focus group. The survey addresses learning styles, attitudes, and issues of plagiarism and collusion. Results show that most students favour the group MSc project, whereas concerns have been raised about possible plagiarism/collusion issues and group arrangements. Results allowed us to develop detailed guidelines for MSc group projects that will be offered in the next academic year

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics

In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle activity logs from 204 students. This analysis identifies early resource engagement as a predictive indicator of continued engagement while also suggesting that the new incentives disproportionately benefit highly engaged students. Focus group discussions complement this analysis, providing insights into student perceptions of the pedagogical changes and the module design. These qualitative findings underscore the challenge of sustaining engagement through the new incentives and highlight the importance of communication in blended learning environments. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives. This model provides educators with a tool to evaluate and improve instructional strategies. By demonstrating the effectiveness of our mixed methods approach in capturing the intricacies of student behaviour in digital learning environments, we underscore the model's potential to improve online pedagogical practices across diverse educational settings.Comment: 5 pages, 1 figure. Accepted by IEEE Global Engineering Education Conference 202

Acute Pulmonary Edema After Double Mechanical Valve Replacement

We report a case of a 16-year-old man in cardiogenic shock secondary to On-X mitral prosthesis dysfunction due to leaflet embolization through aortic mechanical prosthesis. He underwent an emergency redo mitral valve replacement and, a few days later, leaflet removal by open aortic surgery with full recovery