Spread of the Novel Coronavirus (SARS-CoV-2): Modeling and Simulation of Control Strategies

  1. Harishankar Prabhakaran

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Abstract

The coronavirus disease 2019 (COVID-19) is spreading throughout the world and all healthcare systems are loaded beyond its capacity. The virus is named as SARS-CoV-2. In this situation, rational decisions need to be made on how the care is provided for patients with COVID-19. The Incidence report, general symptoms and readily available testing kits, different control strategies, the basic compartmental model, and some of the current research on the epidemiology of the disease are discussed and previously published models are reviewed. Modeling this disease helps in understanding the spread, and predict its future to evaluate different control strategies (Social Distancing, Contact Tracing and Hospitalization). Compartmental modeling framework is used in this work. The non-linear equations are formulated and fitted to the cumulative case and mortality data. Analytical analysis along with uncertainty analysis and sensitivity analysis is performed, and the conditions to achieve disease free equilibrium is evaluated. Finally, Different control strategies are simulated to show their importance. This paper aims to shows the advantage of mathematical modeling and their simulations in times like now, during which the COVID-19 spreading like wildfire. It also includes Pre-symptomatic and asymptomatic individuals in the modeling. The simulations are performed for the model fit to Cumulative Case and Mortality data in the United States of America. The Reproduction number is found to be 2.71914.

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    SciScore for 10.1101/2020.05.11.20098418: (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
    Parameter fitting was performed using nonlinear least squares algorithm im-plemented using the lsqcurvefit function in MATLAB.
    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.11.20098418 on medRxiv