A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts

  1. Yao-Yu Yeo
  2. Yao-Rui Yeo
  3. Wan-Jin Yeo

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Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2000 and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.

Author Summary

Coronavirus disease 2019 (COVID-19) has escalated into a global pandemic and has recently emerged in the US. While some countries have managed to contain COVID-19 efficiently, other countries previously thought to have been well-prepared for outbreaks due to higher living standards and healthcare quality have witnessed an unexpected number of cases. It is currently unclear how the US can cope with the COVID-19 pandemic, especially after mishaps during the initial stages. Our study combines conditions unique to the US and transmission dynamics in regions affected most by COVID-19 to produce a quasi-worse-case scenario of COVID-19 in the US and shows the importance of healthcare preparedness during pandemics.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.24.20043026: (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 simulated our model in MATLAB to prepare for a quasi-worst-case-scenario.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  2. Version published to 10.1101/2020.03.24.20043026v2 on medRxiv
  3. Version published to 10.1101/2020.03.24.20043026v1 on medRxiv
  4. Version published to 10.1017/exp.2020.45