Department of Medicine
Disease Modeling | Epidemiology | Immunology and Infectious Disease | Infectious Disease | Mathematics | Microbiology | Virus Diseases
Since the early reports of COVID-19 cases in China in late January 2020 (1-2), the worst pandemic in 100 years has spread to the entire globe with approximately 2.4 million diagnosed cases and over 165,000 deaths up to April 20, 2020.
While scientists from various public and private groups use math and computer to simulate the course of this pandemic to try to predict how this outbreak might evolve (3), most of such analyses are either quite complicated or not publicly available.
Here a simple mathematic modeling approach is taken to track the outbreaks of COVID-19 in the US and its selected states to identify the peak point of such outbreak within a given geographic population, the trend of decreasing numbers of new cases after the peak and the rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The sources of COVID-19 case data are taken from various public websites since not all the data are readily available.
COVID-19, SARS-CoV-2, modeling, United States, Epidemiology
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© 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
Tang Y, Wang S. Mathematic modeling of COVID-19 in the United States. Emerg Microbes Infect. 2020 Dec;9(1):827-829. doi: 10.1080/22221751.2020.1760146. PMID: 32338150. Link to article on publisher's site
Emerging microbes and infections
Tang Y, Wang S. (2020). Mathematic Modeling of COVID-19 in the United States. Coronavirus COVID-19 Publications by UMMS Authors. https://doi.org/10.1080/22221751.2020.1760146. Retrieved from https://escholarship.umassmed.edu/covid19/20
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.