The coronavirus pandemic has shown how difficult it is to communi­cate the facts, risks and un­cer­tain­ties around health topics effec­tively and with trans­parence. In the Risk Communication project group we work with research results and information out of the Robert Koch Institute in order to present them in an appropriate manner to various target groups. To this end, we use methods from empirical cognitive and be­havioural sciences, such as the develop­ment and testing of various communi­cation formats and media.

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Acinetobacter baumannii and related species such as A. pittii and A. nosocomialis are Gram-negative bacteria that appear as opportunistic pathogens in hospitals where they constitute a serious threat especially to immuno-compromised patients. They can cause ventilator-associated pneumonia, infections of wounds, soft-tissue and the urinary tract as well as sepsis and meningitis. Increasingly, they appear as multi-drug resistant pathogens, restricting therapeutic options more and more.

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Among the pathogens causing infectious diseases in humans, those originating from animals (so-called zoonoses) are exceptionally important in terms of their number and pathogenicity.They can represent a serious threat to public health. Our group combines different approaches to investigate the sources and reservoirs of such zoonotic microorganisms, mechanisms of their transmission to humans and their evolutionary pathways.We hereby focus mainly on sub-Saharan Africa, which bears a disproportionately high burden of morbidity and mortality due to infectious diseases and is a hotspot for zoonotic disease emergence.

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Using computational, theoretical and data-driven techniques from physics, computer science, dynamical systems theory, complex networks theory and complexity science we develop computational and theoretical models to improve our understanding of the dynamics, proliferation and evolution of infectious diseases. These models are designed to advance our understanding of basic mechanism and observed phenomena on a fundamental level. Based on mechanistic, data-driven models we design and build large scale computational models and forecast infrastructures that serve as predictive tools in the context of emergent outbreak scenarios.

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"Systems medicine of infectious diseases" aims to better understand infection and transmission mechanisms, as well as evolutionary dynamics, and to describe them using methods from the field of data science and mathematical modeling. This serves both to create basic knowledge and to better evaluate prevention-, treatment- and surveillance strategies. Our work ranges from the mathematical formulation of the problem, the development or adaptation of algorithms, the evaluation on biomedical data to the implementation and publication of open source software.

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