Biostatistics

How will the climate develop, how secure is our energy supply, and what chances does molecular medicine offer? The rapidly increasing amount of data offers new opportunities to address today’s most pressing questions of society, science, and economy. However, data, outcomes and predictions are subject to uncertainties. The goal of the project is to understand these uncertainties through methods of probability theory, and to include them into research and outreach. The project connects Helmholtz researchers from four research fields, including both applied and data science experts.

The prospective COVID-19 Cohort Munich (KoCo19) study aims to collect and analyze meaningful data to better understand the causes, dynamics, and impacts of the SARS-CoV-2 pandemic and to recommend courses of action. The project was initiated in March 2020 under the lead of the Division of Infectious Diseases and Tropical Medicine, and we contribute through statistical analyses and stochastic modelling.

Diffusion processes are a promising instrument to realistically model the time-continuous evolution of phenomena in biology as they combine the advantages of probabilistic models and differential equation models. However, statistical inference for diffusions proves to be challenging in practice. Our research focuses on computational improvements of Bayesian inference methods and applications in molecular biology and cancer research.