Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters

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Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters. / Захаров, Виктор Васильевич ; Балыкина, Юлия Ефимовна; Ilin, Igor; Tick, Andrea .

In: Mathematics, Vol. 10, No. 20, 3725, 11.10.2022.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{fbbfa43fdc0f4c6089282cc024d13fbc,

title = "Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters",

abstract = "The consideration of infectious diseases from a mathematical point of view can revealpossible options for epidemic control and fighting the spread of infection. However, predicting andmodeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemicof a new type of virus based on the example of COVID-19. The important result of the study is thedescription of the principle of dynamic balance of epidemiological processes, which has not beenpreviously used by other researchers for epidemic modeling. The new approach is also based onsolving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered anddead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the futuretrajectories of stochastic parameters, which are called the percentage increase in the total number ofconfirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the consideredindicators of the COVID-19 epidemic, in Russia and European countries, during the first wave ofthe epidemic.",

keywords = "artificial intelligence; balance model; CIR model; COVID-19; forecasting; modeling, CIR model, COVID-19, artificial intelligence, balance model, forecasting, modeling",

author = "Захаров, {Виктор Васильевич} and Балыкина, {Юлия Ефимовна} and Igor Ilin and Andrea Tick",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors.",

year = "2022",

month = oct,

day = "11",

doi = "10.3390/math10203725",

language = "English",

volume = "10",

journal = "Mathematics",

issn = "2227-7390",

publisher = "MDPI AG",

number = "20",

}

RIS

TY - JOUR

T1 - Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters

AU - Захаров, Виктор Васильевич

AU - Балыкина, Юлия Ефимовна

AU - Ilin, Igor

AU - Tick, Andrea

PY - 2022/10/11

Y1 - 2022/10/11

N2 - The consideration of infectious diseases from a mathematical point of view can revealpossible options for epidemic control and fighting the spread of infection. However, predicting andmodeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemicof a new type of virus based on the example of COVID-19. The important result of the study is thedescription of the principle of dynamic balance of epidemiological processes, which has not beenpreviously used by other researchers for epidemic modeling. The new approach is also based onsolving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered anddead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the futuretrajectories of stochastic parameters, which are called the percentage increase in the total number ofconfirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the consideredindicators of the COVID-19 epidemic, in Russia and European countries, during the first wave ofthe epidemic.

AB - The consideration of infectious diseases from a mathematical point of view can revealpossible options for epidemic control and fighting the spread of infection. However, predicting andmodeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemicof a new type of virus based on the example of COVID-19. The important result of the study is thedescription of the principle of dynamic balance of epidemiological processes, which has not beenpreviously used by other researchers for epidemic modeling. The new approach is also based onsolving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered anddead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the futuretrajectories of stochastic parameters, which are called the percentage increase in the total number ofconfirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the consideredindicators of the COVID-19 epidemic, in Russia and European countries, during the first wave ofthe epidemic.

KW - artificial intelligence; balance model; CIR model; COVID-19; forecasting; modeling

KW - CIR model

KW - COVID-19

KW - artificial intelligence

KW - balance model

KW - forecasting

KW - modeling

UR - http://www.scopus.com/inward/record.url?scp=85140585124&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e73c5bea-6918-33ba-b39c-834df996f17e/

U2 - 10.3390/math10203725

DO - 10.3390/math10203725

M3 - Article

VL - 10

JO - Mathematics

JF - Mathematics

SN - 2227-7390

IS - 20

M1 - 3725

ER -

ID: 100039025