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    Mathematical Modeling Study of the 2020 CoVID-19 Outbreak in the United States

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    Sinha-PrePrint-2020-04.pdf
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    Genre
    Pre-print
    Date
    2020-04-12
    Author
    Sinha, Durgesh cc
    Klahn, Nicholas
    Department
    Mathematics
    Subject
    COVID-19
    US population
    Mathematical Modeling
    SARS-CoV-2
    2019-nCoV
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/228
    
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    DOI
    https://doi.org/10.2139/ssrn.3573877
    Abstract
    A mathematical model was developed for the currently evolving COVID-19 outbreak. Data analysis and model fitting using Latin Hypercube Sampling partial Correlation Coefficient Method was used to determine the model’s parameters and basic reproduction numbers. The infectivity values from symptomatic infectious people was 0.118461389 (95% CI [0.1136278, 0.12329497]), asymptomatic transmission was 0.100111427 (95% CI [0.1000297, 0.10019314]), and quarantined transmission was 0.057337278 (95% CI [0.0504738, 0.0642008]). The United states reached its peak basic reproduction number on March 10th where R0=58, but it has since lowered to 1.47 as of April 5th. Also, those in quarantine had contributed the most to the basic reproduction number, with asymptomatic people being second, and regular symptomatic people contributing the least. Our simulations showed that the United States has reached its peak occurred on April 11, 2020 with a total 461,700 number of cases and it will reach on June 12, 2020 where the confirmed case count would reach 1.439 million. As for the longevity of the virus, our prediction shows that it could be under preventive measure within two years by February 10, 2022, would be 14,130.
    Citation
    Sinha, Durgesh and Klahn, Nicholas, Mathematical Modeling Study of the 2020 CoVID-19 Outbreak in the United States (April 12, 2020). Available at SSRN: https://ssrn.com/abstract=3573877 or http://dx.doi.org/10.2139/ssrn.3573877
    Citation to related work
    SSRN Electronic Journal
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/212
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    COVID-19 Research

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