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Open AccessArticle

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

1
Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Universitetskaya Naberezhnaya 7–9, 199034 Saint Petersburg, Russia
2
School of Automation, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
3
School of Mathematics and Statistics, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1727; https://doi.org/10.3390/math8101727
Received: 7 September 2020 / Revised: 29 September 2020 / Accepted: 30 September 2020 / Published: 8 October 2020
(This article belongs to the Special Issue Machine Learning and Data Mining in Pattern Recognition)
Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%. View Full-Text
Keywords: modeling; forecasting; COVID-19; case-based reasoning; heuristic modeling; forecasting; COVID-19; case-based reasoning; heuristic
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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.

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