Working Group Digital and mathematical Epidemiology

Mathematical models can help to understand the spread of infectious diseases. For this purpose, epidemics are simulated on the computer. It is important to stress the fact that we cannot simply measure the spread of epidemics in a laboratory, since we cannot release pathogens in a population intentionally just to study their spread. Mathematical models and simulations are therefore the only possibility to perform an epidemic experiment at all.

We address the following questions in the working group:

  • How do novel infectious diseases spread?
  • How can we detect outbreaks efficiently in time and with blanket coverage?
  • Which counter measures are promising?
  • What is the impact of climate change on the spread of today exotic infectious diseases? How is the prediction?
  • How can we use artificial intelligence for animal disease control?

Methods

Complex systems

  • Complex networks. Epidemics can potentially spread very fast through livestock trade networks. Data on such networks are available. In the working group, we develop novel network-analysis tools and use existing ones.
  • Dynamical models. The dynamics of epidemics can be modelled using nonlinear dynamic systems. In the working group, we investigate such models spatially as well as in combination with livestock trade (network epidemiology).

Machine Learning

  • Computer Vision. It is the aim of computer vision (CV) to teach the computer to see like a human. This includes the ability to recognize and track an object. Such techniques are promising for the automated monitoring of animal behavior as early indicators for possible diseases.

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