A Particle-Based COVID-19 Simulator with Contact Tracing and Testing

Askat Kuzdeuov, Aknur Karabay, Daulet Baimukashev, Bauyrzhan Ibragimov, Huseyin Atakan Varol

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.

Original languageEnglish
Article number9372866
Pages (from-to)111-117
Number of pages7
JournalIEEE Open Journal of Engineering in Medicine and Biology
Publication statusPublished - 2021


  • contact tracing
  • COVID-19
  • epidemic simulator
  • particle-based simulation
  • random testing

ASJC Scopus subject areas

  • Biomedical Engineering


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