Spatio–Temporal Methods in Environmental Epidemiology with R

Professor Gavin Shaddick is the Executive Dean of the School of Engineering, Mathematical and Physical Sciences and a Professor of Data Science and Statistics at Royal Holloway, University of London and a Turing Fellow at The Alan Turing Institute. His research interests lie at the interface of statistics, AI, epidemiology and environmental science. He is a member of the UK government’s Committee on the Medical Effects of Air Pollutants (COMEAP) and the sub-group on Quantification of Air Pollution Risk (QUARK). He leads the World Health Organization’s Data Integration Taskforce for Global Air Quality and led the development of the Data Integration Model for Air Quality (DIMAQ) that is used to calculate a number of air pollution related to United Nations Sustainable Development Goals indicators. Professor James V. Zidek is Professor Emeritus at the University of British Columbia. He received his M.Sc. and Ph.D. from the University of Alberta and Stanford University, both in Statistics. His research interests include the foundations of environmetrics, notably on the design of environmental monitoring networks, and spatio-temporal modelling of environmental processes. His contributions to statistics have been recognized by a number of awards including Fellowships of the ASA and IMI, the Gold Medal of the Statistical Society of Canada and Fellowship in the Royal Society of Canada, one of that country’s highest honors for a scientist. Professor Alex Schmidt has joined the Shaddick-Zidek team of co-authors. She is Professor of Biostatistics at McGill University. She is distinguished for her work in the theory of spatio-temporal modelling and more recently for that in biostatistics as well as epidemiology. In recognition of that work, she received awards from The International Environmetrics Society (TIES) and the American Statistical Association’s Section on Statistics and the Environment (ENVR-ASA). She was the 2015 President of the International Society for Bayesian Analysis and Chair of the Local Organizing Committee for the 2022 ISBA meeting. Her current topics of research include non-normal models for spatio-temporal processes and the analysis of joint epidemics of dengue, Zika and chikungunya in Latin America.

1. An overview of spatio-temporal epidemiology and knowledge discovery. 2. An introduction to modelling health risks and impacts. 3. The importance of uncertainty: assessment and quantification. 4. Extracting information from data. 5. Embracing uncertainty: the Bayesian approach. 6. Approaches to Bayesian computation. 7. Strategies for modelling. 8. The challenges of working with real-world data. 9. Spatial modelling: areal data. 10. Spatial modelling: point-referenced data. 11. Modelling temporal data: time series analysis and forecasting. 12. Bringing it all together: modelling exposures over space and time. 13. Causality: issues and challenges. 14. The quality of data: the importance of network design. 15. Further topics in spatio-temporal modelling.