14:00 Chris Wymant (Big Data Institute, University of Oxford)
Analysis of pathogen genetic sequence data to help prevent the spread of infectious diseases
Infectious diseases kill millions of people every year. Epidemiological studies of these diseases try to identify patterns that are associated with disease spread, so that we can more effectively intervene and improve public health. Molecular epidemiology uses molecular data for this aim; in particular, the genetic sequence of the pathogen from infected individuals. Sequences accumulate mutations over time, and so such data allow us to make inferences about the pathogen's evolutionary history and perhaps about factors affecting it, i.e. the story of the epidemic from the pathogen's point of view. After a general introduction to this field of work I will explain our molecular epidemiological method 'phyloscanner', some applications of it to large HIV datasets, what we learned, the statistical models involved, and ways in which we would like these models to be better.
15:30 Julia Brettschneider (Department of Statistics, University of Warwick)
Spatial statistics in scientific research involving image data
Progress in imaging technologies has opened up new avenues for scientific research. Statistical methodology needs to be adapted and extended to optimally exploit the available data and address questions formulated by scientists. Interdisciplinary dialog about new types of data can also lead to better models of the measurement process, to improved preprocessing and quality assessment of the data and to novel methods of knowledge extraction and models of the measurement process. In X-ray CT, for example, planar point processes are a natural model for dead pixels. Concepts such as complete spatial randomness can for example be explored with functions capturing between point interactions. They can be used to make statements and inference about the state of the detectors. In fluorescent confocal microscopy, a central interest is the imaging of protein concentration. Distances between point clouds can be captured, for example, using the earth movers distance. In cell biology, this can be used to model relative abundance of two protein species or to describe and analysis of the temporal evolution of a single protein.