Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning

  1. Bo Peng
  2. Rowland W Pettit
  3. Christopher I Amos

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Abstract

Objectives

We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations.

Materials and methods

Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations.

Results

The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans.

Discussion

Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters.

Conclusion

Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.13.20064253: (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
    This simulator is provided as a Python package with a command-line interface.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A limitation of the simulator is that it does not simulate physical locations, so the transmission of virus is performed by probability, not by actual physical vicinity or contact. This design avoids a large number of model assumptions such as population boundaries and individual movement patterns and allows much more efficient simulations of large populations. It however makes it currently infeasible to simulate scenarios such as transmission through common traffic hubs such as schools and supermarkets. Despite this limitation, the current version of the software could be useful for studying the behavior of outbreaks affecting many environments in which individuals are well mixed. We have been asked to provide guidance for a wide variety of industries, including shipping, mining, actors and laboratory environments for which a limited population may interact regularly. Our simulator is effective for identifying how risk can be managed and what kinds of mitigation provide acceptable controls for these situations. We are expanding the simulator, mostly by adding more plugins, to accommodate more scenarios. The software is available through GitHub and further modifications can be made by us or other users.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1093/jamiaopen/ooaa074
  3. Version published to 10.1101/2020.04.13.20064253v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.04.13.20064253: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This simulator is provided as a Python package with a command-line interface.
    Python
    suggested: (IPython, SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  5. Version published to 10.1101/2020.04.13.20064253v1 on medRxiv