THE MODELING NETWORK
The Modeling Network for Severe Infectious Diseases is a research network funded by the Federal Ministry of Education and Research (BMBF) consisting of ten research networks, three of which are associated, and a superordinate coordination office, which was launched in May 2022.
Since the end of 2019, a new type of virus has had the world firmly in its grip. The SARS-CoV-2 pathogen can cause respiratory diseases and pneumonia and has claimed the lives of numerous people worldwide. The coronavirus pandemic is having a significant impact on our everyday lives, depending on the incidence of infection, and is bringing about drastic changes. However, SARS-CoV-2 is not the first virus to trigger a pandemic and will not be the last.
With regard to the current situation, the modeling network for serious infectious diseases with its models and forecasts is the direct point of contact for inquiries from politicians.
Why is modeling important?
Data-based modeling is an elementary tool for a timely and targeted response in the fight against pathogens. They link existing knowledge, make it interpretable and enable predictions to be made about the dynamics of the spread of infectious diseases. This includes not only medical factors, but also social factors such as contact behavior or containment measures. The more data is incorporated into a model, the more accurate the prediction can be.
The assumptions on which the modeling is based are also crucial. In order to make statements that are as reliable and robust as possible, it is essential to involve experts from different scientific disciplines in the selection of these model assumptions and parameters. Machine learning methods can further improve the modeling.
Both in the current COVID-19 pandemic and for future pandemics, modeling provides the scientific basis for political decisions and intervention measures and therefore has a direct impact on our lives.
Goals and tasks
The network has set itself the goal of strengthening the interdisciplinary exchange of leading scientists from the relevant research disciplines. The aim is to sustainably increase modeling expertise in Germany. To this end, the coordination office organizes regular events such as workshops, annual conferences and summer schools. The research results of these meetings are regularly published in joint publications.
The findings of the participating research networks are highly relevant not only for policy advice but also for overarching issues. Cooperation with other research networks such as the German Center for Lung Research, the University Medicine Network Initiative (NUM) or the Zoonosis Platform should therefore be actively promoted. The generated modeling knowledge is made available to all partners.
The development of common standards for data management and storage is essential for the comprehensive provision and use of research data and is an important part of the modeling network’s tasks.
In addition to making research results available, publishing them for the general dissemination of knowledge is also an important goal. The focus here is on the comprehensible communication and correct interpretation of the model statements, as well as the presentation of the underlying mathematical methods, including an explanation of the limits of their significance.
In addition, the network is committed to promoting young scientists in order to strengthen modeling expertise in Germany in the long term. The format of the summer schools is specifically aimed at training these young modelers. At the same time, experienced scientists are developing general standards in conceptual workshops, as well as teaching and training programs for the promotion of young talent. In this way, structured training in the field of mathematical modeling can be established in medical studies or in epidemiological and public health training.
At a glance
- Policy advice
- Promoting the exchange and networking of the scientific community
- Strengthening cooperation with other research networks
- Provision of findings for overarching issues
- Development of common standards for data management and storage
- Publication of research results for the general dissemination of knowledge
- Development of joint teaching and training programs for junior researchers
