Postdoctoral research associate on Spreading phenomena on geometric networks

Employer: Rényi Institute of Mathematics

Place: Budapest, Hungary

Research theme: epidemic modeling, network science, graph theory, geometric networks, metapopulation models

Scientific directors: Dr. Márton Karsai ( ) & Prof. Dr. László Lovász ( )

Network Epidemics Group @ Rényi Insitute

The Network Epidemics Group at the Rényi Institute works on the mathematical, computational, and data-driven modelling of dynamical epidemiological processes on graphs and networks. On one hand, the group plays special focus on the mathematical foundation of geometric network effects on evolving spreading processes, and on the other hand, on the data-driven simulations of epidemic processes to observe and understand real-world spreading phenomena. The group is led by Dr. Márton Karsai and Pr. László Lovász and functions as a member of the Health Security National Laboratory in Hungary.


It is a fundamental question in disease modeling how the structure and dynamics of social interactions and mobility mixing patterns influence an ongoing epidemic. These behavioral patterns can be effectively represented as networks, that provide effective tools for the mathematical and computational modelling of epidemic phenomena. They contribute to a better approximation that incorporates non-homogeneous mixing patterns within and between populations, which can build up into meta-population networks to describe how epidemics spread in countries or even around the globe.

The geometric structure and spatial organization of interaction and mobility networks play special roles in the emergence of a rich but largely unexplored set of spreading phenomena. One of these phenomena is the commonly observed spatial clustering of infection cases during the sub-sequent waves of the actual COVID-19 pandemic. While these phenomena can be related to the inhomogeneous spatial distribution of susceptible populations, local patterns of herd immunity or the different seeding scenarios of an actual wave, their emergence is substantially depending on the geometric nature of the underlying social and mobility networks.

In this project we aim to tackle this problem from two different directions:

Computational modelling of epidemic processes on geometric networks: to develop a spatially embedded meta-population framework, relying on data from Hungary, that is capable to reproduce rich class of spatially clustered patterns of infected cases in the country.
Mathematical modelling: to develop the mathematical foundation of these observed phenomena by identifying the fundamental graph properties of the underlying network structures that can induce the observed geometric patterns of infection clustering.

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