Prof. Christiane Fuchs (Bielefeld University & Helmholtz Munich)

Title: Integrative modelling of infections in a corona virus cohort study

Abstract: Research on statistical modelling holds various potentials: on the one hand, it enables the further development of methodologically sophisticated models and inference; on the other hand, models may be applied to real data and lead to relevant knowledge gains in the applied domain. In practice, compromises must be made if approaches other than the theoretical ideals prove to be the most expedient.

Since the outbreak of the corona pandemic, many mathematical and statistical approaches have been used to identify drivers of infection and disease, to advise decision makers, and to predict the further course. However, models that worked well in idealized contexts often encountered limitations in real-world situations; these include unreliable information on numbers of infected individuals, and a rapidly changing environment regarding control measures, varying testing and vaccination capacities and strategies, weather conditions, and new virus variants.

I will report from our work in the KoCo19 Consortium, which has established a representative cohort for the Munich population in private households and has collected and analyzed laboratory and questionnaire data in several rounds since April 2020. We estimate the prevalence of SARS-CoV-2 in the population and combine different data sources to reliably estimate the effectiveness of non-pharmaceutical intervention methods. This is done using a variety of statistical methods, not least differential equations and Bayesian modelling. Ultimately, the project reveals synergies between the methods and application, despite or perhaps because of the compromises made.

The talk will take place in UHG V2-210/16 at 3pm.