Geo-information system of tuberculosis spread based on inversion and prediction

Standard

Geo-information system of tuberculosis spread based on inversion and prediction. / Kabanikhin, Sergey ; Krivorotko, Olga; Takuadina, Aliya и др.

в: Journal of Inverse and Ill-Posed Problems, Том 29, № 1, 01.02.2021, стр. 65-79.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Kabanikhin, S , Krivorotko, O, Takuadina, A, Andornaya, D & Zhang, S 2021, 'Geo-information system of tuberculosis spread based on inversion and prediction', Journal of Inverse and Ill-Posed Problems, Том. 29, № 1, стр. 65-79. https://doi.org/10.1515/jiip-2020-0022

APA

Kabanikhin, S., Krivorotko, O., Takuadina, A., Andornaya, D., & Zhang, S. (2021). Geo-information system of tuberculosis spread based on inversion and prediction. Journal of Inverse and Ill-Posed Problems, 29(1), 65-79. https://doi.org/10.1515/jiip-2020-0022

Vancouver

Kabanikhin S , Krivorotko O, Takuadina A, Andornaya D, Zhang S. Geo-information system of tuberculosis spread based on inversion and prediction. Journal of Inverse and Ill-Posed Problems. 2021 февр. 1;29(1):65-79. Epub 2020 сент. 1. doi: 10.1515/jiip-2020-0022

Author

Kabanikhin, Sergey ; Krivorotko, Olga ; Takuadina, Aliya и др. / Geo-information system of tuberculosis spread based on inversion and prediction. в: Journal of Inverse and Ill-Posed Problems. 2021 ; Том 29, № 1. стр. 65-79.

BibTeX

@article{dd8b92c34acd4c1bb901e99177450e8c,

title = "Geo-information system of tuberculosis spread based on inversion and prediction",

abstract = "The monitoring, analysis and prediction of epidemic spread in the region require the construction of mathematical model, big data processing and visualization because the amount of population and the size of the region could be huge. One of the important steps is refinement of mathematical model, i.e. determination of initial data and coefficients of system of differential equations of epidemiologic processes using additional information. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization approach. Our algorithms are applied to analysis and prediction of epidemic situation in regions of Russian Federation, Republic of Kazakhstan and People's Republic of China. Due to a great amount of data we use a special software {"}Digital Earth{"}for visualization of epidemic. ",

keywords = "Digital Earth, epidemiology, genetic algorithm, Inverse problem, optimization, prediction",

author = "Sergey Kabanikhin and Olga Krivorotko and Aliya Takuadina and Darya Andornaya and Shuhua Zhang",

year = "2021",

month = feb,

day = "1",

doi = "10.1515/jiip-2020-0022",

language = "English",

volume = "29",

pages = "65--79",

journal = "Journal of Inverse and Ill-Posed Problems",

issn = "0928-0219",

publisher = "Walter de Gruyter GmbH",

number = "1",

}

RIS

TY - JOUR

T1 - Geo-information system of tuberculosis spread based on inversion and prediction

AU - Kabanikhin, Sergey

AU - Krivorotko, Olga

AU - Takuadina, Aliya

AU - Andornaya, Darya

AU - Zhang, Shuhua

PY - 2021/2/1

Y1 - 2021/2/1

N2 - The monitoring, analysis and prediction of epidemic spread in the region require the construction of mathematical model, big data processing and visualization because the amount of population and the size of the region could be huge. One of the important steps is refinement of mathematical model, i.e. determination of initial data and coefficients of system of differential equations of epidemiologic processes using additional information. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization approach. Our algorithms are applied to analysis and prediction of epidemic situation in regions of Russian Federation, Republic of Kazakhstan and People's Republic of China. Due to a great amount of data we use a special software "Digital Earth"for visualization of epidemic.

AB - The monitoring, analysis and prediction of epidemic spread in the region require the construction of mathematical model, big data processing and visualization because the amount of population and the size of the region could be huge. One of the important steps is refinement of mathematical model, i.e. determination of initial data and coefficients of system of differential equations of epidemiologic processes using additional information. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization approach. Our algorithms are applied to analysis and prediction of epidemic situation in regions of Russian Federation, Republic of Kazakhstan and People's Republic of China. Due to a great amount of data we use a special software "Digital Earth"for visualization of epidemic.

KW - Digital Earth

KW - epidemiology

KW - genetic algorithm

KW - Inverse problem

KW - optimization

KW - prediction

UR - http://www.scopus.com/inward/record.url?scp=85092414612&partnerID=8YFLogxK

U2 - 10.1515/jiip-2020-0022

DO - 10.1515/jiip-2020-0022

M3 - Article

AN - SCOPUS:85092414612

VL - 29

SP - 65

EP - 79

JO - Journal of Inverse and Ill-Posed Problems

JF - Journal of Inverse and Ill-Posed Problems

SN - 0928-0219

IS - 1

ER -

ID: 25611291