Uncovering Chains of Infection/Inferring Infection Chains

BSc/MSc Project Seminar - Winter 2020/21

People: Prof. Dr. Tobias Friedrich, Philipp Fischbeck, Maximilian Katzmann, Karen Seidel, Christopher Weyand, Ziena Zeif
Links: Lehrveranstaltungen IT-Systems Engineering Bachelor/Master, Algorithm Engineering Moodle

This lecture will be held virtually via Zoom. To access the corresponding link and for organizational details, please subscribe until 05 November 2020 to our course moodle. In case of problems with subscribing to our moodle, please get back to the lecture team via e-mail. Note that the moodle subscription is additionally required to the official course subscription via the Studienreferat. As the number of participants is limited, for acceptance we go by the registration via the Studienreferat. The moodle subscription must be done before the first meeting on 6 November 2020 at 9:15 am.

The current Sars-CoV2-Pandemic is an example for fast spreading infectious diseases in our modern, connected world. The tracking of infection chains helps to identify and isolate sources of infection, superspreaders and facilitates future prevention.

In addition to epidemiological contact tracing, molecular methods gain importance in order to reveal transmission paths that were not recognized or disclosed. The similarity of the continuously changing pathogen genomes suggests infection chains. While there is a variety of bioinformatical methods to analyze and visualize the molecular raw data (e.g. nextstrain.org), reliable computational models and techniques to infer a infection chain from the similarity of the pathogen genomes are missing.

The goal of the project is to develop and implement algorithms that infer possible infection chains. Such infection chains form trees in the corresponding weighted graph. One of the challenges is to infer those chains by starting from mere genome sequences. A comparison of the sequenced RNA yields a similarity/distance matrix between the genomes. A variety of approaches will be tested on different simulated and real-world data sets. We also aim at a rigorous analysis of their correctness and asymptotic run-time behaviour.

In correspondence with the research group for Data Analytics & Computational Statistics and the department for Methodology and Research Infrastructure at the Robert Koch Institute the algorithms can be evaluated on relevant data sets for Sars-CoV2 and other infectious diseases.

The participants will actively participate in the steps of the process of algorithm development for a real-world problem. This includes reviewing and understanding existing tools and how they can be used in the process. The goal is to develop and implement algorithms, rigorously prove their correctness and analyze relevant theoretical properties. A talk of 20-30 mins and a report of 8-12 pages about a previously agreed topic determine the grade in proportion 1:2.

The course will be managed via our Moodle, and enrolment in this Moodle course is required for course participation. We meet fridays 9:15-10:45am, starting on 6 November. Due to the current situation, we meet online and the link to our discussion room is announced in Moodle.