# CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Related Works

#### 2.2. Preliminary Insight on Recurrent Relations

#### 2.3. Case-Based Rate Reasoning Method

## 3. Results

#### 3.1. Modeling for the USA

#### 3.2. Modeling for Russia

#### Simulation Results for Russia

## 4. Discussion

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Steps for constructing the forecast trajectory of the coronavirus epidemic dynamics based on the Case-Based Rate Reasoning (CBRR) approach.

**Figure 3.**Growth rate over the considered time period in the USA (real-world data and model forecast). Red dotted lines correspond to the growth rates of 5% and 2%.

**Figure 4.**Total number of confirmed cases in the Russian Federation (RF) (real-world data and model forecast).

**Figure 5.**Growth rate over the considered time period in the RF (real-world data and model forecast). Red dotted lines correspond to the growth rates of 5% and 2%.

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**MDPI and ACS Style**

Zakharov, V.; Balykina, Y.; Petrosian, O.; Gao, H. CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time. *Mathematics* **2020**, *8*, 1727.
https://doi.org/10.3390/math8101727

**AMA Style**

Zakharov V, Balykina Y, Petrosian O, Gao H. CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time. *Mathematics*. 2020; 8(10):1727.
https://doi.org/10.3390/math8101727

**Chicago/Turabian Style**

Zakharov, Victor, Yulia Balykina, Ovanes Petrosian, and Hongwei Gao. 2020. "CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time" *Mathematics* 8, no. 10: 1727.
https://doi.org/10.3390/math8101727