The Controversies in the Data Society seminar series is presented by the Edinburgh Futures Institute in association with Science Technology and Innovation Studies.
The next seminar in our 2021 series is Data and Model and Public Communication for Covid Policy.
Professor Chris Dent: “Communicating Covid data – how to be uncertain well”.
In this talk, Professor Chris Dent will cover the different topics in the title to illustrate a number of features of being ‘uncertain well’. He will speak about managing Covid under uncertainty about how it will develop.
About the speaker:
Prof Chris Dent is Personal Chair in Industrial Mathematics in the School of Mathematics at the University of Edinburgh and held research and academic positions at Heriot-Watt, Marburg, Edinburgh and Durham Universities. https://www.maths.ed.ac.uk/school-of-mathematics/people/a-z?person=524
In addition to his main appointment at Edinburgh, he is a Turing Fellow at the Alan Turing Institute: https://www.turing.ac.uk/people/researchers/chris-dent
His research interests include the application of data science across energy system and planning. He particularly focuses on how approaches from data science can productively be taken to practical application in government and industry.
Dr Ben Swallow: Multivariate spatio-temporal analysis of the global COVID-19 pandemic
Ben will present work done with Wen Xiang that can be read below. The Covid-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging global trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global Covid-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality dynamics from early-August, consistent in timing with the emergence of new strains with higher levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.
Dr Ben Swallow, Lecturer in Statistics, University of Glasgow. Research interests are Bayesian inference in environmental and biological sciences. http://ben-swallow-research.github.io
Ms Wen Xiang, final year undergraduate on the BSc Double Degree in Statistics with ZUEL at the University of Glasgow. Wen did a summer research project, from which this research resulted.
This event is a live discussion between the speakers and audience which follows the pre-recorded seminars. The seminar videos can be viewed here:
Please note this live discussion will be recorded.