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

  1. Askat Kuzdeuov
  2. Aknur Karabay
  3. Daulet Baimukashev
  4. Bauyrzhan Ibragimov
  5. Huseyin Atakan Varol

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Abstract

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.

Impact Statement

Our particle-based epidemic simulator, calibrated with COVID-19 data, models each individual as a unique particle with a location, velocity, and epidemic state, enabling the consideration of contact tracing and testing measures.

Article activity feed

  1. Version published to 10.1101/2020.12.07.20245043v2 on medRxiv
  2. Version published to 10.1109/ojemb.2021.3064506
  3. ScreenIT

    SciScore for 10.1101/2020.12.07.20245043: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We implemented the simulator in MATLAB R2020.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Even though the modeling of individuals as particles enables the implementation of contact tracing and massive testing, our simulator has several limitations. Firstly, our map is a unit square with the individuals distributed randomly and moving freely without obstacles. In real world, there are obstacles, e.g. buildings and geographic objects such as rivers and mountains. Also, the population density differs substantially in different regions of a city or province. The probability of infection is also lower in open spaces than in confined ones. Secondly, the mortality rate for COVID-19 is age and gender dependent [25]. Our particles are identical, i.e. demographics properties such as age and gender are not considered. Presumably, the simulator can be enriched by adding the demographics profiles and related risk probabilities for more realistic transitions from the Severe Infected to Dead state. However, this would increase the number of simulation parameters significantly and make the model calibration harder. Thirdly, the simulator also does not consider the interaction networks of individuals. However, in reality, individuals have a number of contacts with whom they interact regularly, e.g. family members, colleagues, and close friends.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.12.07.20245043v1 on medRxiv