Seminar: COVID-19 — Distributed Risk Response

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


With the onset of COVID-19 as a world pandemic, social contact plays an important role in the spread of the virus. When a person is tested positive for the virus, the risk of infection of that person’s social network increases. As a mean to slow down the spread of the virus, experts propose to remove individuals at risk from the general population. Some countries started deploying centralised systems to notify, identify, and request to self-isolate individuals at risk, based on their social connections.


The goal of the seminar is to build a distributed messaging system that enables a user to update the risk of their network in the case of a status change.

Learning Objectives

  • Gain an overview of edge computing and decentralized messaging frameworks.
  • Understand concepts of privacy, global consistency and distributed ledgers.
  • Apply messaging frameworks, and compare performance for safety and accuracy.
  • Create a distributed health risk calculator which enables users to notify others that they might be at risk of being infected, and they should self-isolate.

Course Data

Project type Seminar (optional: Bachelor or Master thesis)
Language English
Period Summer semester 2020
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, see: online-introduction-vorbesprechung-of-summer-term-2020-seminars
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