SynoSys, along with a consortium of leading researchers, has secured funding for the DREAM EP (Data-informed Responsive Epidemic Analysis and Multiscale-Modelling for Epidemic Preparedness) research project. The initiative, funded with a total of €1.8 million, will support top-level research in pandemic forecasting, with €300,000 allocated to SynoSys. The project aims to improve the prediction of severe respiratory disease outbreaks by integrating data on human contact structures, mobility patterns, and protective behaviors. By analyzing high-resolution datasets from the COVID-19 pandemic, the project will develop a comprehensive model ecosystem that captures relevant spatial and temporal scales using methodologies from network science, machine learning, and artificial intelligence.

The project is coordinated by Prof. Dr. Dirk Brockmann at the TUD (University of Technology Dresden), Center Synergy of Systems. Research contributions come from a strong consortium including Prof. Dr. Thilo Gross (Alfred Wegener Institute), Prof. Dr. Bernd Blasius (Carl von Ossietzky University Oldenburg), Prof. Dr. Christian Drosten (Charité Berlin), Prof. Dr. Vitaly Belik (Free University Berlin) and Prof. Dr. Thorsten Lehr (Saarland University). These partners bring expertise in epidemiology, virology, statistical physics, public health, and computational modeling to ensure a comprehensive approach to the challenges of pandemic forecasting.

By leveraging extensive datasets, including SARS-CoV-2 virus evolution, daily mobility data from Germany, and global air traffic patterns, DREAM EP aims to create an adaptive modeling framework. This interdisciplinary approach will enable a better understanding of how pandemic dynamics influence human behavior and how these behavioral changes, in turn, affect disease spread. The project will address fundamental questions of scale and feedback in epidemiological modeling, contributing to more accurate and data-driven forecasting tools.

Expected Outcomes and Innovations

DREAM EP will deliver multiple innovative models and analyses, including:

• A quantitative assessment of how spatial mobility networks evolve during a pandemic and their impact on disease dynamics.
• A data-driven investigation into how micro-scale contact networks and clinical outcomes shape respiratory infection dynamics under varying social behaviors and interventions.
• A comprehensive analysis of causal relationships between disease spread, information dissemination, behavioral responses, and intervention policies.
• The development of optimal model architectures for predicting epidemic dynamics based on structured, responsive, multi-scale networks.
• The identification of principles governing viral evolution in network-structured populations and their influence on epidemic dynamics.
• A scalable, deployable framework for forecasting severe respiratory disease outbreaks in Germany.

By integrating computational techniques such as machine learning, network analysis, causal inference, deep learning, and hypergraph-based analysis, DREAM EP will establish a solid modeling platform. The project will improve epidemic forecasting, particularly in the early stages of an outbreak, and enhance long-term pandemic response strategies.