TY - GEN

T1 - Start of Epidemy in a City

T2 - 15th IEEE International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020

AU - Boldyreva, Anna

AU - Alexandrov, Mikhail

AU - Koshulko, Olexiy

AU - Popova, Svetlana

N1 - Publisher Copyright: © 2020 IEEE.

PY - 2020/9/23

Y1 - 2020/9/23

N2 - The sudden onset and quick development of an unknown epidemic may lead to tragic consequences: panic of population due to victims and unpreparedness of authorities for effectively help to population. These circumstances define extremely high requirements to the tools for short-term operational forecast. Namely, such tools should provide reliable results when model of phenomenon is unknown (factors of disease spreading) and data are limited (time series of observations). GMDH-based algorithms just meet these requirements unlike modern differential or advanced statistical models. In this study we test different algorithms from GMDH Shell platform on the example of Covid-19 epidemic in Moscow during the period March 30-April 12, 2020. The forecast horizon is from 1 to 7 days, the initial information is only the official dynamics of diseased patients. Our model is autoregression with variables of different powers. The results of forecast are compared with the accuracy of popular statistical autoregression using exponential smoothing with trend. We suppose that the proposed approach will be useful for short-term forecast at the start of epidemic due to its simplicity and reliability.

AB - The sudden onset and quick development of an unknown epidemic may lead to tragic consequences: panic of population due to victims and unpreparedness of authorities for effectively help to population. These circumstances define extremely high requirements to the tools for short-term operational forecast. Namely, such tools should provide reliable results when model of phenomenon is unknown (factors of disease spreading) and data are limited (time series of observations). GMDH-based algorithms just meet these requirements unlike modern differential or advanced statistical models. In this study we test different algorithms from GMDH Shell platform on the example of Covid-19 epidemic in Moscow during the period March 30-April 12, 2020. The forecast horizon is from 1 to 7 days, the initial information is only the official dynamics of diseased patients. Our model is autoregression with variables of different powers. The results of forecast are compared with the accuracy of popular statistical autoregression using exponential smoothing with trend. We suppose that the proposed approach will be useful for short-term forecast at the start of epidemic due to its simplicity and reliability.

KW - Covid-19

KW - epidemic

KW - GMDH

KW - GMDH Shell

KW - short-term forecast

UR - http://www.scopus.com/inward/record.url?scp=85100495936&partnerID=8YFLogxK

U2 - 10.1109/CSIT49958.2020.9322033

DO - 10.1109/CSIT49958.2020.9322033

M3 - Conference contribution

AN - SCOPUS:85100495936

T3 - International Scientific and Technical Conference on Computer Sciences and Information Technologies

SP - 5

EP - 8

BT - 2020 IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020 - Proceedings

Y2 - 23 September 2020 through 26 September 2020

ER -