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TU Berlin

Inhalt des Dokuments

Andreas Koher, M.Sc.


Institut für Theoretische Physik
ER 240 (Ernst Ruska Gebäude)
Sekr. EW 7-1
Institut für Theoretische Physik
Technische Universität Berlin
Hardenbergstr. 36
D-10623 Berlin, Germany

Fields of interest

  • (Temporal) network analysis
  • Stochastik processes with application to intra-cellular transport and disease spreading
  • Nonlinear dynamics and epidemiological models


Risk Assessment from Past Temporal Data


For more information, have a look at our slides (PDF, 8,4 MB) from the NetSciX conference 2018 in Hangzhou.

Effective distances and disease spreading on complex networks

Visualisation of the global airtraffic networ by Lukas Kikuchi. The nodes correspond to airports and links are airline connections. We used data from OpenFlights.org

Forecast and control of global epidemic outbreaks such as SARS (2003), H1N1 (2009) and Ebola (2014) remains a major challenge to public health institutions. One of the most relevant information for containment strategies is the time until the first infected individual arrives at a specific place. Due to the complex nature of human mobility, however, the spreading patterns of infectious diseases in geographical space appears erratic and unpredictable. Here, we provide a mathematical framework from the perspective of random-walk theory that allows us to uncover the close connection between a simple diffusion process and epidemics spreading on complex networks. In particular, we find that the infection arrival time at every node in the network can be estimated by the hitting time probability of a random walker that started at the outbreak location. Detailed numerical simulations on the global air-traffic network confirm our results.

For more information, you can read our publication, view our poster (PDF, 1,4 MB) and slides (PDF, 3,5 MB) from the NetSci 2016 conference or play around with our code on GitHub.

Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany

A visualisation of the German pig trade network by Lukas Kikuchi. Please follow the link to find the interactive version.

Animal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. For more information, please check our publication on PlosOne. Interactive visualisations of the German pig trade network by Lukas Kikuchi can be found here: 1, 2, 3, 4.

Transport on lattices with dynamic and static disorder


We analyse diffusive transport on a lattice with static and fluctuating bonds, which generalizes two well known results: We recover  Kirkpatrick's (1973) famous result on static bond percolation and the dynamic percolation model of Harrison and Zwanzig (1985), respectively. Our model of mixed percolation allows to study diffusivity in dynamically changing environments close to the percolation threshold. The effective medium approximation provides a close estimate of the diffusivity that we obtain from extensive Monte-Carlo simulations.

Slides from the DPG spring meeting 2017 can be downloaded here (PDF, 1,2 MB).

Infections on Temporal Networks - A Matrix-Based Approach


We extend the concept of accessibility in temporal networks from Lentz et al. to model infections with a finite infectious period such as the susceptible-infected-recovered (SIR) model. This approach is entirely based on elementary matrix operations and unifies the disease and network dynamics within one algebraic framework. We demonstrate the potential of this formalism for three examples of networks with high temporal resolution: networks of social contacts, sexual contacts, and livestock-trade.

For more information, you can download the publication, view our poster (PDF, 4,8 MB) and play around with the code on GitHub.

Data based risk assessment



Zusatzinformationen / Extras