SHS Web of Conf.
Volume 92, 2021The 20th International Scientific Conference Globalization and its Socio-Economic Consequences 2020
|Number of page(s)||7|
|Section||Global Impact of COVID 19 on Economy and Society|
|Published online||13 January 2021|
A SEIRD Model for Control of COVID-19: Case of Azerbaijan
Azerbaijan State University of Economics, Department of Digital Economy and ICT, Istiglaliyyat 6, AZ1001 Baku, Azerbaijan
* Corresponding author: firstname.lastname@example.org
Research background: The study uses the key parameters of the spread of the epidemic, dividing the population into several groups S - susceptible, E - exposed, I - infectious, R - recovered, D - dead. It is found that the model behaves differently depending on the R₀ indicator - the average number of people that one infected manages to infect. Measures to suppress the epidemic undertaken by Azerbaijan and their effectiveness have been considered.
Purpose of the article: The aim of the article is to model the current dynamics of the disease for future forecasting. The model takes into account all the main parameters of the epidemic: the proportion of severe patients and the mortality rate depending on the age of the patients, the duration of the incubation period and the infectious phase of the disease; incomplete registration of infected people due to the high prevalence of asymptomatic disease and insufficient testing; possible measures to contain and suppress the epidemic and their impact on R₀.
Methods: The article uses the linear regression method, which consists in finding estimates of unknown parameters and the formation of a functional relationship between the sickness rate and the factors determining it.
Findings & Value added: The constructed model analyzes the growth of patients in the country after removing the restrictive measures taken in early May on the basis of real statistics.
Key words: COVID-19 / SEIRD / regression model / differential equations / exponential growth
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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