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Mathematic modeling of COVID-19 in the United States. Can we rely on this? μ(t)=μ(t0)exp(αt)

Emerg Microbes Infect. 2020 Dec;9(1):827-829 Mathematic modeling of COVID-19 in the United States. Tang Y1, Wang S2. Author information 1 Applied NanoFemto Technologies, LLC, Lowell, MA, USA. 2 Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA. Abstract COVID-19, the worst pandemic in 100 years, has rapidly spread to the entire world in 2 months since its early report in January 2020. Based on the publicly available data sources, we developed a simple mathematic modeling approach to track the outbreaks of COVID-19 in the US and three selected states: New York, Michigan and California. The same approach is applicable to other regions or countries. We hope our work can stimulate more effort in understanding how an outbreak is developing and how big a scope it can be and in what kind of time framework. Such information is critical for outbreak control, resource utilization and re-opening of the normal daily life to citizens in the affected community. KEYWORDS: COVID-19; SARS-CoV-2; United States; epidemiology; modeling


Figure 1. (A) Cumulative COVID-19 cases in USA. Daily plot with reported cases (blue) and predicted cases (red), and the predicted total COVID-19 cases at the end of June 2020. (B) Daily growth rate of COVID-19 cases in USA. Actual daily growth rate (blue curve), 5-day moving average of the growth rate (black curve) and exponential fix and predicted growth rate (red curve) are shown. (C) Daily new COVID-19 cases in USA. Reported numbers are in blue and predicted numbers are in red. (D) Cumulative COVID-19 cases in Michigan. Daily plot with reported cases (blue) and predicted cases (red), and the predicted total COVID-19 cases at the end of June 2020. (E) Daily growth rate of US COVID-19 cases in Michigan. Actual daily growth rate (blue curve), 5-day moving average of the growth rate (black curve) and exponential fix and predicted growth rate (red curve) are shown. (F) Daily new COVID-19 cases in Michigan. Reported numbers are in blue and predicted numbers are in red. (G) Cumulative COVID-19 cases in New York. Daily plot with reported cases (blue) and predicted cases (red), and the predicted total COVID-19 cases at the end of June 2020. (H) Daily growth rate of COVID-19 cases in New York. Actual daily growth rate (blue curve), 5-day moving average of the growth rate (black curve) and exponential fix and predicted growth rate (red curve) are shown. (I) Daily new COVID-19 cases in New York. Reported numbers are in blue and predicted numbers are in red. (J) Cumulative COVID-19 cases in California. Daily plot with reported cases (blue) and predicted cases (red), and the predicted total COVID-19 cases at the end of June 2020. (K) Daily growth rate of COVID-19 cases in California. Actual daily growth rate (blue curve), 5-day moving average of the growth rate (black curve) and exponential fix and predicted growth rate (red curve) are shown. (L) Daily new COVID-19 cases in California. Reported numbers are in blue and predicted numbers are in red.


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