UMMS Affiliation

Department of Medicine

Publication Date

2020-12-01

Document Type

Article

Disciplines

Disease Modeling | Epidemiology | Immunology and Infectious Disease | Infectious Disease | Mathematics | Microbiology | Virus Diseases

Abstract

We previously described a mathematical model to simulate the course of the COVID-19 pandemic and try to predict how this outbreak might evolve in the following two months when the pandemic cases will drop significantly. Our original paper prepared in March 2020 analyzed the outbreaks of COVID-19 in the US and its selected states to identify the rise, peak, and decrease of cases within a given geographic population, as well as a rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The current report will describe how well the later actual trend from March to June fit our model and prediction. Similar analyses are also conducted to include countries other than the US. From such a wide global data analysis, our results demonstrated that different US states and countries showed dramatically different patterns of pandemic trend. The values and limitations of our modelling are discussed.

Keywords

COVID-19, SARS-CoV-2, epidemiology, modelling, pandemic

Rights and Permissions

© 2020 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DOI of Published Version

10.1080/22221751.2020.1843973

Source

Tang Y, Tang S, Wang S. The values and limitations of mathematical modelling to COVID-19 in the world: a follow up report. Emerg Microbes Infect. 2020 Dec;9(1):2465-2473. doi: 10.1080/22221751.2020.1843973. PMID: 33121387; PMCID: PMC7671649. Link to article on publisher's site

Journal/Book/Conference Title

Emerging microbes and infections

Related Resources

Link to Article in PubMed

PubMed ID

33121387

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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