Jan is a PhD candidate in the Centre for Doctoral Training on Intelligent and Autonomous Machines and Systems (AIMS CDT) supervised by Yarin Gal, and funded by Cancer Research UK and the FHI DPhil Scholars program. His current research interests include AI safety (in particular interpretability of machine learning algorithms), applications of AI in medicine and biomedical research, and clinical research on cognitive enhancement.
Jan studied medicine at the University of Erlangen-Nuremberg and the University of Wuerzburg, Germany. Parallel to studying, he worked as a research assistant in neuroscience, immunology and global health. After graduating from medical school, he finished a one-year master’s degree in Operational Research with Data Science at the University of Edinburgh and worked with Prof. Amos Storkey on innovative deep learning-based approaches to medical image analysis.
There remains much uncertainty about the relative effectiveness of different nonpharmaceutical interventions (NPIs) against COVID-19 transmission. Several studies attempt to infer NPI effectiveness with cross-country, data-driven modelling, by linking from NPI implementation dates to the observed timeline of cases and deaths in a country. These models make many assumptions. Previous work sometimes tests the sensitivity to variations in explicit epidemiological model parameters, but rarely analyses the sensitivity to the assumptions that are made by the choice the of model structure (structural sensitivity analysis). Such analysis would ensure that the inferences made are consistent under plausible alternative assumptions. Without it, NPI effectiveness estimates cannot be used to guide policy. We investigate four model structures similar to a recent state-of-the-art Bayesian hierarchical model. We find that the models differ considerably in the robustness of their NPI effectiveness ... [full abstract]
Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna B. Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
Existing analyses of nonpharmaceutical interventions (NPIs) against COVID19 transmission have concentrated on the joint effectiveness of large-scale NPIs. With increasing data, we can move beyond estimating joint effects towards disentangling individual effects. In addition to effectiveness, policy decisions ought to account for the burden placed by different NPIs on the population. Methods: To our knowledge, this is the largest data-driven study of NPI effectiveness to date. We collected chronological data on 9 NPIs in 41 countries between January and April 2020, using extensive fact-checking to ensure high data quality. We infer NPI effectiveness with a novel semi-mechanistic Bayesian hierarchical model, modelling both confirmed cases and deaths to increase the signal from which NPI effects can be inferred. Finally, we study how much perceived burden different NPIs impose on the population with an online survey of preferences using the MaxDiff method. Results: Eight NPIs have a >... [full abstract]
Jan Brauner, Sören Mindermann, Mrinank Sharma, Anna B Stephenson, Tomáš Gavenčiak, David Johnston, John Salvatier, Gavin Leech, Tamay Besiroglu, George Altman, Hong Ge, Vladimir Mikulik, Meghan Hartwick, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal, Jan Kulveit