Working Groups of the Clinical Epidemiology Unit
- Infectious Disease Epidemiology
- Epidemiological Methods
- Observational Study Platform
Infectious Disease Epidemiology
Infectious diseases remain a leading cause of morbidity and mortality worldwide. New pathogens continue to emerge, causing unforeseen outbreaks leading to events like the influenza pandemic in 2009 and the SARS-CoV-2 pandemic. Infectious disease epidemiology (IDEpi) provides key tools for the assessment and response to these health threats in populations.
A major focus of the IDEpi group is on improving the parametrisation of infectious disease models via the collection and extensive analysis of real-world epidemiological data, especially with respect to contact patterns (GetCoSy, OptimAgent, Respinow). This allows us to develop better-informed mathematical models that can answer relevant public health questions and provide valuable guidance to decision-makers in various contexts, including in healthcare settings (ARCANE, CARE-FLOW, InnoBRI, Opti-ITS), during epidemic and pandemic situations (TwinChain, PREPARED, EpiAdaptDiag), and for understanding the spread of vector-borne diseases (VBD-MODE).
We collaborate with many national and international partners in academia, medicine, and the public health system and are part of the German Modelling Network for Severe Infectious Diseases (MONID) and the Interdisciplinary Center for Mathematical Modeling of Infectious Disease Dynamics (IMMIDD). These collaborations enable us to bring together expertise from different disciplines, providing a well-rounded research perspective and increasing the public health impact of our research. We aim to strengthen future collaboration between public health authorities and academia (INFRALINK), and to improve teaching and training of current and future researchers and medical doctors through courses on infectious disease epidemiology and modelling at various levels nationally and internationally (Spring School IDEpi), as well as international capacity building by establishing higher level education focusing on infectious disease epidemiology (NOZGEKA).
All in all, we aim to produce high-quality research in infectious disease epidemiology which improves the health of human populations.
Epidemiological Methods
Our working group develops and evaluates scientific methods to systematically and reliably address clinical research questions. Our goal is to strengthen the evidence base for medical decision-making and to contribute to a better understanding of health and disease in the population.
We focus on methodological and analytical aspects of clinical epidemiology. By developing and applying suitable study designs and statistical approaches, we aim to provide valid answers to clinical research questions – for example, how to minimize bias and confounding or how to assess the reliability of diagnostic tests. Close collaborations with university hospitals and research institutes help us identify clinically relevant topics that can ultimately improve patient care (ChilSFree).
In medical practice, many decisions rely on estimated probabilities – such as how likely a disease is present (diagnostic setting) or whether a specific event may occur in the future (prognostic setting). We apply modern statistical methods, including machine learning, to address medical prediction problems (ELISE) and develop new techniques for reliably estimating the diagnostic accuracy of medical tests (ClusterDiag).
One example is the early and accurate detection of infections. This is not only relevant for determining an individual’s health status but also for model-based evaluation of infection control measures at the population level. In collaboration with the “Infectious Disease Epidemiology” working group, we study methods to evaluate diagnostic test accuracy (EpiAdaptDiag).
Another central focus of our work is the microbiome – the community of microorganisms inhabiting the human body. These microbial communities play an essential role in maintaining health by shaping the immune system and supporting physiological balance. We investigate how the composition of the microbiome relates to health and disease, both in patient cohorts and in population-based studies.
In addition, using approaches from causal inference, we examine other factors that may influence an individual’s susceptibility to infections or post-infectious conditions (RESOLVE-PCC).
Observational Study Platform
We provide a technical, methodological, data collection and biosampling infrastructure for clinicians and potential other cooperation partners that allows the planning, practical conduct and analysis of observational studies. The different components are linked in an openly available Observational Study Platform. Within this platform, we offer support:
- - in the planning of observational studies
- - a study centre for the practical performance of population-based studies
- - access to a variety of primary data sources available at the institute (PROCAM-2, BiDirect, NAKO)
- - support in getting access to secondary data sources linked to the institute (e.g., Health Insurance Data, Cancer Registry Data)
- - support in accessing biosamples available at the 'Zentrale Biobank der Medizinischen Fakultät Münster' (ZBB-MFM)
- - support in the analysis of observational studies using modern statistical and epidemiological models
The two central projects currently running in the working group are the NAKO Health Study and ZEBra-MSP, the evaluation of the German Mammography Screening Program.