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Project Group P4: Epidemiologically Modeling of Infectious Diseases

Dirk Brockmann

Prof. Dr. Dirk Brockmann
Dr. Chen Li
Olga Baranov
Benjamin Maier
Frank Schlosser
Gina Polo
Stephan Adler
Nikolas Brunner
Thea Dennell
Peer Heinrich
Lars Zerbe


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. We develop models with a wide range of applications and scales.

Global Mobility and Emergent Infectious Diseases

The most probable spreading routes for Ebola, during the Ebola crisis. © Brockmann / RKI Using methods from network science, data on worldwide air-transportation we developed a computational interactive tool for computing the most probable spreading routes for Ebola, during the Ebola crisis. The node on the bottom represents the Conakry airport CKY, the lines the most probable import routes to other locations.

On one end of the spectrum we develop large-scale computational models that target global aspects of disease dynamics, predominantly the geographic spread of diseases or how relative import risks are globally distributed as a function of outbreak locations. For instance, we have developed a class of models that incorporate the entire global air-transportation system including the passenger flux along more than 25000 connections between 4000 airports worldwide, amounting to a global traffic of over three billion passengers per year.

Because mobility is a key factor in global dissemination of emergent diseases we apply new techniques from network theory to understand how hidden structural features of global mobility networks shape the expected spread or distribution of import risks across the globe. These quantitative, early risk-assessment methods are extremely useful in the early phase of an outbreak when little information on the specific situation is available in order to get a holistic approximate assessment of what is expected concerning the time-course of an outbreak.

Big Data and Digital Epidemiology

Currently, novel technological developments, trackable items, social media, personal health monitors, and smart devices open new possibilities and opportunities for measuring contact patterns in large scale populations. The collection and assessment of very precise data obtained by these methods is a major shift in epidemiological analysis because data of this resolution is generally not accessible by traditional surveys or cohort studies. Digital epidemiology, the measurement of individual based contact patterns with a very high spatiotemporal precision embedded in large scale natural experiments, for instance electronic contact tracing using RFID technology in hospital settings, has become a powerful technique for understanding transmission pathways in the context of hospital acquired diseases.

Using this type of Big Data we reconstruct temporal contact networks, applying methods from complex network theory to unravel structural patterns and to identify which types of networks are particularly susceptible to spreading a disease. In combination with sequence data obtained by next generation sequencing technology we develop techniques that improve our understanding of transmission mechanisms in hospitals and other contexts. Based on this data we develop mechanistic models for disease dynamics on temporal contact networks in order to improve predictive and complement statistical models usually applied in traditional epidemiology.

Date: 24.05.2016


  • Wells DK, Chuang Y, Knapp LM, Brockmann D et al. (2015): Spatial and functional heterogeneities shape collective behavior of tumor-immune networks.
    PLoS Comput. Biol. 11 (4): e1004181. Epub Apr 23. doi: 10.1371/journal.pcbi.1004181. more

  • Helbing D, Brockmann D et al. (2014): Saving human lives: what complexity science and information systems can contribute.
    J. Stat. Physics: Epub Jun 6. doi: 10.1007/s10955-014-1024-9. more

  • Lemey P, Rambaut A, Bedford T, Faria N, Bielejec F, Baele G, Russell CA, Smith DJ, Pybus OG, Brockmann D et al. (2014): Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
    PLoS Pathog. 10 (2): e1003932. Epub Feb 20. doi: 10.1371/journal.ppat.1003932. more

  • Brockmann D, Helbing D (2013): The hidden geometry of complex, network-driven contagion phenomena.
    Science 342 (6164): 1337-1342. Epub Dec 13. doi: 10.1126/science.1245200. more

  • Belik V, Geisel T, Brockmann D (2011): Natural human mobility patterns and spatial spread of infectious diseases.
    Phys. Rev. X 1, 011001. more