Preface

This project aims to simulate the COVID-19 pandemic in Vorarlberg to better understand the dynamics of such a disease on a local scope in a region which the students are very familiar with. In this project several issues are investigated both in terms of simulation, and in disease dynamics. More specifically this project aims at building a small model of Vorarlberg, based on open data to investigating the efficacy of various intervention strategies. As simulation tool the high-quality scientific simulation library Covasim, written in Python, will be used, which brings all the basic functionality and has to be adopted by the students for the tasks in the project.

The primary focus in this project is on modeling an Agent-Based Simulation, understanding the dynamics and the consequence of various intervention strategies. It is explicitly not the aim to develop new insights and make novel contributions to the field, as it is clearly beyond the focus of this course.1

The whole class will be split into groups of 3-5, to work on the topics and research questions outlined for this project. Although initial topics, research questions and basic resources are provided for the students, it is expected that the groups are working highly autonomous, investigating the respective topics and research questions on their own with the provided materials. The lecturer will act as a supervisor, guiding the work of the groups where necessary and moderating discussions between groups and of the class as a whole. Therefore, the outcome of this project is quite open, not determined in advance and consequently there are no “TODO lists” of what is expected from the students to get good marks. What is important and relevant to this project is the learning experience. Therefore, the assessment is based upon the degree of collaboration in this project as individual and as a group and the depth and autonomy by which the topics and research questions are investigated - it can be therefore be seen as a group-based precursor to a master thesis.

Learning Goals

  • Understanding of the dynamics of an infectious disease by means of simulation.
  • Understanding the efficacy and consequences of various intervention strategies.
  • Development of a model of Vorarlberg, based on existing data.
  • Reading of Papers and transferring knowledge to a project in a rigorous manner.
  • Writing of a technical report.
  • Scientific Computing
  • Network Effects
  • Verification & Validation
  • Implementation techniques of ABS

Project Course Content

Other, more general topics are handled in the accompanying lecture and are drawn from the respective lecture notes.

Timeline

  1. Project Wednesday 16 Sep, 08:10 - 09:45
    • Introduction
    • Pandemics: History, Today, Corona, Interventions
    • How To Read a Paper
    • Familiarise with Covasim
  2. Lecture Wednesday 16 Sep, 09:50 - 11:25
    • Input: Compartment Models
  3. Project Wednesday 23 Sep, 08:10 - 09:45
    • How To Write
    • Familiarise with Covasim
  4. Lecture Wednesday 23 Sep, 09:50 - 11:25
    • Working on Project
  5. Project Wednesday 30 Sep, 08:10 - 09:45
    • Model of Vorarlberg
  6. Lecture Wednesday 30 Sep, 09:50 - 11:25
    • Input: Modeling
  7. Project Wednesday 7 Oct, 08:10 - 09:45
    • Model of Vorarlberg
  8. Lecture Wednesday 7 Oct, 09:50 - 11:25
    • Working on Project
  9. Project Wednesday 21 Oct, 08:10 - 09:45
    • Herdimmunity vs. Social Distancing
  10. Lecture Wednesday 21 Oct, 09:50 - 11:25
    • Input: ABS Implementation Techniques
  11. Project Wednesday 28 Oct, 08:10 - 09:45
    • Herdimmunity vs. Social Distancing
  12. Lecture Wednesday 28 Oct, 09:50 - 11:25
    • Working on Project
  13. Project Wednesday 4 Nov, 08:10 - 09:45
    • Herdimmunity vs. Social Distancing
  14. Lecture Wednesday 4 Nov, 09:50 - 11:25
    • Input: Verification and Validation
  15. Project Wednesday 11 Nov, 08:10 - 09:45
    • Digital Tracing
  16. Lecture Wednesday 11 Nov, 09:50 - 11:25
    • Working on Project
  17. Project Wednesday 18 Nov, 08:10 - 09:45
    • Digital Tracing
  18. Lecture Wednesday 18 Nov, 09:50 - 11:25
    • Input: Complex Networks
  19. Project Wednesday 25 Nov, 08:10 - 09:45
    • Digital Tracing
  20. Lecture Wednesday 25 Nov, 09:50 - 11:25
    • Working on Project
  21. Project Wednesday 2 Dec, 08:10 - 09:45
    • Digital Tracing
  22. Lecture Wednesday 2 Dec, 09:50 - 11:25
    • Input: Monte Carlo Simulation
    • Exchange of Reports for constructive critical review of all groups among each other (and myself)
  23. Project Wednesday 9 Dec, 08:10 - 09:45
    • Vaccination
  24. Lecture Wednesday 9 Dec, 09:50 - 11:25
    • Working on Project
    • Constructive and critical discussion of all reports
  25. Project Wednesday 13 Jan, 08:10 - 09:45
    • Vaccination
  26. Lecture Wednesday 13 Jan, 09:50 - 11:25
    • Working on Project
    • Reading of Simulation Argument Paper to discuss the following week
  27. Project Wednesday 20 Jan, 08:10 - 09:45
    • Vaccination
  28. Lecture Wednesday 20 Jan, 09:50 - 11:25
    • Input: Philosophy & Epistemology and Simulation Argument
  29. Project Wednesday 27 Jan, 08:10 - 09:45
    • Final Presentations and Discussions
  30. Lecture Wednesday 27 Jan, 09:50 - 11:25
    • Final Presentations and Discussions

Assessment

Contribution to project, presentations, technical report and oral group examination.


  1. However, in case a student is interested in investigating a specific research question or interesting dynamics more in depth, this could be done in the form of a master thesis.↩︎