Predicting the dynamics of the coronavirus (COVID-19) epidemic based on the case-based reasoning approach
Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 16 (2020) no. 3, pp. 249-259
Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The case-based rate reasoning (CBRR) method is presented for predicting future values of the coronavirus epidemic's main parameters in Russia, which makes it possible to build short-term forecasts based on analogues of the percentage growth dynamics in other countries. A new heuristic method for estimating the duration of the transition process of the percentage increase between specified levels is described, taking into account information about the dynamics of epidemiological processes in countries of the spreading chain. The CBRR software module has been developed in the MATLAB environment, which implements the proposed approach and intelligent proprietary algorithms for constructing trajectories of predicted epidemic indicators.
Keywords: modeling, forecasting, COVID-19 epidemic, percentage rate of increase, case-based reasoning, heuristic.
@article{VSPUI_2020_16_3_a2,
     author = {V. V. Zakharov and Yu. E. Balykina},
     title = {Predicting the dynamics of the coronavirus {(COVID-19)} epidemic based on the case-based reasoning approach},
     journal = {Vestnik Sankt-Peterburgskogo universiteta. Prikladna\^a matematika, informatika, processy upravleni\^a},
     pages = {249--259},
     year = {2020},
     volume = {16},
     number = {3},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VSPUI_2020_16_3_a2/}
}
TY  - JOUR
AU  - V. V. Zakharov
AU  - Yu. E. Balykina
TI  - Predicting the dynamics of the coronavirus (COVID-19) epidemic based on the case-based reasoning approach
JO  - Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
PY  - 2020
SP  - 249
EP  - 259
VL  - 16
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/VSPUI_2020_16_3_a2/
LA  - ru
ID  - VSPUI_2020_16_3_a2
ER  - 
%0 Journal Article
%A V. V. Zakharov
%A Yu. E. Balykina
%T Predicting the dynamics of the coronavirus (COVID-19) epidemic based on the case-based reasoning approach
%J Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
%D 2020
%P 249-259
%V 16
%N 3
%U http://geodesic.mathdoc.fr/item/VSPUI_2020_16_3_a2/
%G ru
%F VSPUI_2020_16_3_a2
V. V. Zakharov; Yu. E. Balykina. Predicting the dynamics of the coronavirus (COVID-19) epidemic based on the case-based reasoning approach. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 16 (2020) no. 3, pp. 249-259. http://geodesic.mathdoc.fr/item/VSPUI_2020_16_3_a2/

[1] Novel Coronavirus Global Research and Innovation Forum: Towards a Research Roadmap, , WHO (accessed: June 29, 2020) www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov

[2] S. P. Layne, J. M. Hyman, D. M. Morens, J. K. Taubenberger, “New coronavirus outbreak: Framing questions for pandemic prevention”, Sci. Transl. Med., 12:534 (2020), eabb1469 | DOI

[3] J. T. Wu, K. Leung, G. M. Leung, “Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study”, Lancet, 395:10225 (2020), 689–697 | DOI

[4] Models of Infectious Disease Agent Study, MIDAS Coordination Center, (accessed: June 29, 2020) www.midasnetwork.us

[5] M. Mandal, S. Jana, S. K. Nandi, A. Khatua, S. Adak, T. K. Kar, “A model based study on the dynamics of COVID-19: Prediction and Control”, Chaos, Solitons and Fractals, 136 (2020), 109889 | DOI | MR

[6] D. Fanelli, F. Piazza, “Analysis and forecast of COVID-19 spreading in China, Italy and France”, Chaos, Solitons and Fractals, 134 (2020), 109761 | DOI | MR

[7] S. Bekirosab, D. Kouloumpou, “SBDiEM: A new mathematical model of infectious disease dynamics”, Chaos, Solitons and Fractals, 136 (2020), 109828 | DOI | MR

[8] G. D. Barmparis, G. P. Tsironis, “Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach”, Chaos, Solitons and Fractals, 135 (2020), 109842 | DOI

[9] M. A. Kondratyev, “Forecasting methods and models of disease spread”, Computer Research and Modeling, 5:5 (2013), 863–882 | DOI

[10] R. Schmidt, T. Waligora, Influenza forecast: Case-Based Reasoning or statistics?, Proceedings of the 11th International conference on knowledge-based intelligent information and engineering systems, v. I, Lecture Notes in Computer Science, 4692, 2007, 287–294 | DOI | MR

[11] C. Viboud, P. Y. Boelle, F. Carrat, A. J. Valleron, A. Flahault, “Prediction of the spread of influenza epidemics by the method of analogues”, American Journal of Epidemiology, 158:10 (2003), 996–1006 | DOI

[12] Johns Hopkins Coronavirus Resource Center, (accessed: June 29, 2020) https://coronavirus.jhu.edu/data