Geodesic
Statistical forecasting of the dynamics of epidemiological indicators for COVID-19 incidence in the republic of belarus
Journal of the Belarusian State University. Mathematics and Informatics, Tome 3 (2020), pp. 36-50.

Voir la notice de l'article provenant de la source Math-Net.Ru

The paper is devoted to the urgent problem of statistical forecasting for the dynamics of the main epidemiological indicators for the COVID-19 pandemic in the Republic of Belarus based on the observed time series. To solve this problem, five methods are proposed: forecasting method based on «moving trends»; local-median forecasting method; forecasting method based on discrete time series; forecasting method based on the vector econometric error correction model; method of sequential statistical analysis. Algorithms for computation of point and interval forecasts for the main epidemiological indicators have been developed. The numerical results of computer forecasting are presented on the example of the Republic of Belarus.
Keywords: forecasting; probability model; time series; point forecast; interval forecast; COVID-19. 
@article{BGUMI_2020_3_a3,
     author = {Yu. S. Kharin and V. A. Valoshka and O. V. Dernakova and V. I. Malugin and A. J. Kharin},
     title = {Statistical forecasting of the dynamics of epidemiological indicators for {COVID-19} incidence in the republic of belarus},
     journal = {Journal of the Belarusian State University. Mathematics and Informatics},
     pages = {36--50},
     publisher = {mathdoc},
     volume = {3},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/BGUMI_2020_3_a3/}
}
TY  - JOUR
AU  - Yu. S. Kharin
AU  - V. A. Valoshka
AU  - O. V. Dernakova
AU  - V. I. Malugin
AU  - A. J. Kharin
TI  - Statistical forecasting of the dynamics of epidemiological indicators for COVID-19 incidence in the republic of belarus
JO  - Journal of the Belarusian State University. Mathematics and Informatics
PY  - 2020
SP  - 36
EP  - 50
VL  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/BGUMI_2020_3_a3/
LA  - ru
ID  - BGUMI_2020_3_a3
ER  - 
%0 Journal Article
%A Yu. S. Kharin
%A V. A. Valoshka
%A O. V. Dernakova
%A V. I. Malugin
%A A. J. Kharin
%T Statistical forecasting of the dynamics of epidemiological indicators for COVID-19 incidence in the republic of belarus
%J Journal of the Belarusian State University. Mathematics and Informatics
%D 2020
%P 36-50
%V 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/BGUMI_2020_3_a3/
%G ru
%F BGUMI_2020_3_a3
Yu. S. Kharin; V. A. Valoshka; O. V. Dernakova; V. I. Malugin; A. J. Kharin. Statistical forecasting of the dynamics of epidemiological indicators for COVID-19 incidence in the republic of belarus. Journal of the Belarusian State University. Mathematics and Informatics, Tome 3 (2020), pp. 36-50. http://geodesic.mathdoc.fr/item/BGUMI_2020_3_a3/

[1] M. A. Kondratev, “Metody prognozirovaniya i modeli rasprostraneniya zabolevanii”, Kompyuternye issledovaniya i modelirovanie, 5(5) (2013), 863–882 | DOI

[2] R. Hirk, G. Kastner, L. Vana, “Investigating the dark figure of COVID-19 cases in Austria: borrowing from the decode genetics study in Iceland”, Austrian Journal of Statistics, 49(5) (2020), 1–17 | DOI

[3] D. Fanelli, F. Piazza, “Analysis and forecast of COVID-19 spreading in China, Italy and France”, Chaos, Solitons and Fractals, 134 (2020), 109841 | DOI | MR

[4] Y. u. Kharin, “Robustness in statistical forecasting”, New York: Springer, 2013, 356 | MR

[5] YuS. Kharin, V. A. Voloshko, E. A. Medved, “Statistical estimation of parameters for binary conditionally nonlinear autoregressive time series”, Mathematical Methods of Statistics, 27 (2018), 103–118 | DOI | MR

[6] V. A. Voloshko, Yu. S. Kharin, “Semibinomialnye uslovno nelineinye avtoregressionnye modeli diskretnykh sluchainykh posledovatelnostei: veroyatnostnye svoistva i statisticheskoe otsenivanie parametrov”, Diskretnaya matematika, 31(1) (2019), 72–98 | DOI

[7] Y. u. Kharin, M. Zhurak, “Analysis of spatio-temporal data based on Poisson conditional autoregressive model”, Informatica, 26(1) (2015), 67–87 | DOI | MR | Zbl

[8] V. V. Maevskii, YuS. Kharin, “Robust regressive forecasting under functional distortions in a model”, Automation and Remote Control, 63(11) (2002), 1803–1820 | DOI | MR | Zbl

[9] M. A. Pashkevich, YuS. Kharin, “Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data”, Statistics and Operations Research Transactions, 28(2) (2004), 125–160 | MR | Zbl

[10] L. N. Bolshev, N. V. Smirnov, “Tablitsy matematicheskoi statistiki”, Moskva: Nauka, 1983, 512 | MR

[11] Yu. S. Kharin, N. M. Zuev, E. E. Zhuk, “Teoriya veroyatnostei, matematicheskaya i prikladnaya statistika”, Minsk: BGU, 2011, 465

[12] Yu. S. Kharin, “Optimalnost i robastnost v statisticheskom prognozirovanii”, Minsk: BGU, 2008, 265

[13] Y. u. Kharin, “Statistical analysis of discrete-valued time series by parsimonious high-order Markov chains”, Austrian Journal of Statistics, 49(4) (2020), 76–88 | DOI

[14] B. Kedem, K. Fokianos, “Regression models for time series analysis”, Wiley: Hoboken, 2002, 326 | MR

[15] V. I. Malyugin, “Metody analiza mnogomernykh ekonometricheskikh modelei s neodnorodnoi strukturoi”, Minsk: BGU, 2014, 351

[16] V. Colizza, A. Barrat, M. Barthelemy, A. Vespignani, “Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study”, BMC Medicine, 5 (2007), 34 | DOI

[17] R. F. Engle, CWJ. Granger, “Co-integration and error correction: representation, estimation and testing”, Econometrica, 55(2) (1987), 251–276 | DOI | MR | Zbl

[18] W. O. Kermack, A. G. McKendrick, “Contributions to the mathematical theory of epidemics – I”, Bulletin of Mathematical Biology, 53(1–2) (1991), 33–55 | DOI

[19] Yu. S. Kharin, V. I. Malyugin, A. Yu. Kharin, “Ekonometricheskoe modelirovanie”, Minsk: BGU, 2003, 313

[20] S. Johansen, “Likelihood-based inference in cointegrated vector autoregressive models”, Oxford: Oxford University Press, 1995, 267 | MR | Zbl

[21] A. Yu. Kharin, “Robastnost baiesovskikh i posledovatelnykh statisticheskikh reshayuschikh pravil”, Minsk: BGU, 2013, 207

[22] A. Kharin, T. T. Tu, “Performance and robustness analysis of sequential hypotheses testing for time series with trend”, Austrian Journal of Statistics, 46(3–4) (2017), 23–36 | DOI | MR

[23] AYu. Kharin, “An approach to asymptotic robustness analysis of sequential tests for composite parametric hypotheses”, Journal of Mathematical Sciences, 227(2) (2017), 196–203 | DOI | MR