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Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Nov 25, 2021)

Date Submitted: Oct 10, 2021

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Calibrating an SIR model for South Korea COVID-19 infections and predicting vaccination impact

  • Brandon Pae

ABSTRACT

In the span of 1.5 years, COVID-19 has caused more than 4 million deaths worldwide. To prevent such a catastrophe from reoccurring, it is necessary to test and refine current epidemiological models that impact policy decisions. Thus, we developed a deterministic SIR model to examine the long-term transmission dynamics of COVID-19 in South Korea. Using this model, we analyzed how vaccines would affect the number of cases. We found that a 70% vaccination coverage with a 100% effective vaccine would effectively eliminate the number of cases and herd immunity would have been obtained approximately 85 days after February 15 had there not been a reintroduction of cases.


 Citation

Please cite as:

Pae B

Calibrating an SIR model for South Korea COVID-19 infections and predicting vaccination impact

JMIR Preprints. 10/10/2021:34052

DOI: 10.2196/preprints.34052

URL: https://preprints.jmir.org/preprint/34052

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