Research output: Contribution to journal › Article › peer-review
CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time. / Zakharov, Victor; Balykina, Yulia; Petrosian, Ovanes; Gao, Hongwei.
In: Mathematics, Vol. 8, No. 10, 1727, 10.2020.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time
AU - Zakharov, Victor
AU - Balykina, Yulia
AU - Petrosian, Ovanes
AU - Gao, Hongwei
N1 - 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.
PY - 2020/10
Y1 - 2020/10
N2 - 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%.
AB - 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%.
KW - Case-based reasoning
KW - COVID-19
KW - Forecasting
KW - Heuristic
KW - Modeling
UR - http://www.scopus.com/inward/record.url?scp=85093704220&partnerID=8YFLogxK
U2 - 10.3390/math8101727
DO - 10.3390/math8101727
M3 - Article
AN - SCOPUS:85093704220
VL - 8
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 10
M1 - 1727
ER -
ID: 70504500