Ecological Epidemiology - EcoEpi
The Ecological Epidemiology (EcoEpi) team works on ecological models to support decision making in animal, plant and public health. Their speciality is the joint assessment of practicality, economic consequences and public health aspects of crisis decisions within the constraints of the given environment (management-oriented disease modelling – MoDiM). EcoEpi has been working on One Health issues (coined in 2007) since the late 1990s, integrating environmental, animal health and zoonotic risks.
The individual-oriented, rule-based models provide test-beds for contingency planning and control optimisation. EcoEpi modelling studies examine large-scale epidemic patterns based on the ecological fate of individual organisms. The paradigm couples detailed expert knowledge of the diseased organism with dynamics in host populations or at the landscape level. In this way, we transfer detailed scientific understanding to the level of management decisions and policy.
EcoEpi's scenario-based evaluation of computer simulations provides a mechanistic understanding of how host ecology (habitat use, dispersal behaviour, contact structure, reproduction) interacts with disease transmission (infectivity, time scale) and human intervention effort (timing, intensity, approach).
The projects lead to risk assessments, improved decision making, evaluation of management options and policy support. National, EU and WAHO guidelines for health management programmes reflect the insights gained from our modelling. The diversity of projects paves the way for substantial contributions to the ecology of disease and its control, and the development of appropriate modelling techniques.
We have developed a generic framework for implementing expert system models for various pathogen problems, resulting in tailored user applications or sophisticated research tools. We are keen to collaborate with interested users in the field of animal, plant or human health. Specific initiatives include the real-time application of computer simulations in epidemic emergency situations.