Seminar: Particle Simulators for Infection Tracking

Symbolbild zum Artikel. Der Link öffnet das Bild in einer großen Anzeige.
Photo by Liam McKay on Unsplash


Particle models can be used to simulate population behaviour. Using the simulated behaviours, we can analyse the different components that affect spreads of infections. Our current experience with the pandemic has shown that different regions might have a different response to similar intervention strategies. Using the simulator, we can study what the social, cultural, and geographic factors that affect the efficiency of various interventions are. And more importantly, we can evaluate the economical and social impacts of the selected intervention strategies.


Implement an intervention analysis for public spaces and evaluate effects on the population in terms of safe space utilisation, the number of isolations, and the duration of infection wave.

Learning Objectives

  • Gain an overview of dynamic system modelling.
  • Explore and understand the features of human behaviour in public places
  • Apply particle modelling to analyse infection propagation.
  • Create simulator modules to model people behaviour in public space scenarios.

Course Data

Project type Seminar
Language English
Period Winter term 2020/21
Presence time Virtual seminar, working from remote.
Useful knowledge Python, data analytics
Work distribution 40% algorithm development, 30% data analysis and evaluation, 20% consultation, 10% reporting
Med. Eng. designation Advanced Context Recognition (ACR)
StudOn link Please join
First meeting online-introduction-vorbesprechung-of-winter-term-2020-seminars, on 4th November 2020 at 16:15
Registration Via StudOn, obligatory after introduction.

Up-to-date literature recommendations are provided during the lectures.


  • Final project presentation, demonstrator and final report.


Dr. Luis I. Lopera G.

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
  • Phone number: +49 9131 85-23605
  • Email:

Friedrich-Alexander-Universität Erlangen-Nürnberg