A New Data Integration Framework for Covid-19 Social Media Information

The Covid-19 pandemic presents a serious threat to people health, resulting in over 250 million confirmed cases and over 5 million deaths globally. To reduce the burden on national health care systems and to mitigate the effects of the outbreak, accurate modelling and forecasting methods for short- and long-term health demand are needed to inform government interventions aiming at curbing the pandemic. Current research on Covid-19 is typically based on a single source of information, specifically on structured historical pandemic data. Other studies are exclusively focused on unstructured online retrieved insights, such as data available from social media. However, the combined use of structured and unstructured information is still uncharted. This paper aims at filling this gap, by leveraging historical and social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to exploit the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and a big unstructured dataset of information collected from social media. The results show that the combined use of official and online generated information contributes to yield a more accurate assessment of the evolution of the Covid-19 pandemic, compared to the sole use of official data.Comment: arXiv admin note: substantial text overlap with arXiv:2104.0186

Bayesian non-parametric conditional copula estimation of twin data

Several studies on heritability in twins aim at understanding the different contribution of environmental and genetic factors to specific traits. Considering the national merit twin study, our purpose is to analyse correctly the influence of socio-economic status on the relationship between twins’ cognitive abilities. Our methodology is based on conditional copulas, which enable us to model the effect of a covariate driving the strength of dependence between the main variables. We propose a flexible Bayesian non-parametric approach for the estimation of conditional copulas, which can model any conditional copula density. Our methodology extends the work of Wu, Wang and Walker in 2015 by introducing dependence from a covariate in an infinite mixture model. Our results suggest that environmental factors are more influential in families with lower socio-economic position

Internationalisation, cultural distance and country characteristics: a Bayesian analysis of SME's financial performance

Relying on the accounting data of a panel of 403 Italian manufacturing SMEs collected over a period of 5 years, we find results suggesting that multinationality per se does not impact on the economic performance of international small and medium sized firms. It is the characteristics of the country selected i.e. the political hazard, the financial stability and the economic performance that significantly influence SMEs financial performance. The management implication for small and medium sized firms selecting and entering new geographic markets is significant, since our results show that for SMEs it is the market selection process that really matters and not the degree of multinationality

Stakeholder Perspectives on Graphical Tools for Visualising Student Assessment and Feedback Data

This paper contributes to the development of learning and academic analytics in Higher Education (HE) by researching how four graphical visualisation methods can be used to present student assessment and feedback data to five stakeholder groups, including students, external examiners and industrialists. The visualisations and underlying data sets are described, together with the results of a questionnaire designed to elicit the perspectives of the stakeholder groups on the potential value of the visualisations. Key findings of this study are that external examiners agree that the visualisations help them to carry out their role and students concur that they can assist with study organisation, relative performance assessment against the wider cohort and even module choice. All stakeholder groups were positive about the benefits of graphical visualisations in this HE context and supported an increased use of visualisations to assist with data interpretation

Default probability estimation via pair copula constructions

publisher: Elsevier articletitle: Default probability estimation via pair copula constructions journaltitle: European Journal of Operational Research articlelink: http://dx.doi.org/10.1016/j.ejor.2015.08.026 content_type: article copyright: Copyright © 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved

Reducing energy demand in China and the United Kingdom: The importance of energy literacy

As the impacts of climate change become increasingly visible across the globe, awareness of the need for cleaner energy and demand reduction is growing. Energy literacy offers a strong potential for explaining and predicting energy-related behaviours, yet research and policies focused on this topic remain limited. In this study, energy literacy was measured in a sample of 2806 university students in the United Kingdom and China, in addition to their wider environmental attitudes using the New Ecological Paradigm scale. Findings indicate that energy literacy was relatively high overall, but there were significant differences between the knowledge, attitudes and behavioural intentions of participants in the two countries. Whilst the UK respondents rated themselves significantly more highly on perceived knowledge of energy issues, Chinese respondents provided significantly more correct answers in a knowledge test. UK respondents demonstrated more positive attitudes towards energy conservation than those from China, and were more likely to report energy-saving behaviours. However, Chinese respondents exhibited higher levels of trust in government and businesses to take action on energy issues. This paper provides a novel insight into cultural differences which may be crucial to policy and practice, and evidences the potential benefits of utilising a combination of educational and structural change to support transition to a cleaner, low-energy society

A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?

University courses in statistical modeling often place great emphasis on methodological theory, illustrating it only briefly by means of limited and repeatedly used standard examples. Unfortunately, this approach often fails to actively engage and motivate students in their learning process. The teaching of statistical topics such as Bayesian survival analysis can be enhanced by focusing on innovative applications. Here we discuss the visualization and modelling of a data set of historical events comprising the post–election survival times of popes. Inference, prediction and model checking are performed in the Bayesian framework, with comparisons being made with the frequentist approach. Further opportunities for similar statistical investigations are outlined