About Covasim project

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions.Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility.Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors.Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute.

In this Student project our focus was on the Germany population with the SIR model and we used Covasim as a stochastic agent-based simulator for performing COVID-19 analyses. Our Global sensitivity analysis method:

Our calibration method:

Nelder-Mead

Powel

Software requirements

Covasim https://docs.idmod.org/projects/covasim/en/latest/modules.html

SALib https://salib.readthedocs.io/en/latest/

Prerequisites:

Numpy library https://numpy.org/

Scipy library https://scipy.org/

Matplotlib library https://matplotlib.org/

Repository stores

Each folder consists of the final report and source code.

Possible scenarios of Covid dynamics with different interventions

Sensitivity Analysis of four Covasim input parameters

Calibration of the Covasim model to data from Germany

References

Covasim: http://paper.covasim.org

Controlling COVID-19 via test-trace-quarantine: https://doi.org/10.1101/2020.07.15.20154765

Rostock University

The department of computer science at the university of Rostock has a great course about data driven simulation. During the course the students had to achieve milestones. The tasks for the milestones are given in `Project_Tasks.pdf`.

Mentors:

Prof. Dr. rer. nat. habil Adelinde M. Uhrmacher adelinde.uhrmacher@informatik.uni-rostock.de

M.Sc. Pia Wilsdorf pia.wilsdorf@uni-rostock.de