Diagnostic model for the society safety under COVID-19 pandemic conditions
Journal article
Varotsos, Costas A., Krapivin, Vladimir F. and Xue, Yong 2021. Diagnostic model for the society safety under COVID-19 pandemic conditions. Safety Science. 136, p. 105164. https://doi.org/10.1016/j.ssci.2021.105164
| Authors | Varotsos, Costas A., Krapivin, Vladimir F. and Xue, Yong |
|---|---|
| Abstract | The aim of this paper is to develop an information-modeling method for assessing and predicting the consequences of the COVID-19 pandemic. To this end, a detailed analysis of official statistical information provided by global and national organizations is carried out. The developed method is based on the algorithm of multi-channel big data processing considering the demographic and socio-economic information. COVID-19 data are analyzed using an instability indicator and a system of differential equations that describe the dynamics of four groups of people: susceptible, infected, recovered and dead. Indicators of the global sustainable development in various sectors are considered to analyze COVID-19 data. Stochastic processes induced by COVID-19 are assessed with the instability indicator showing the level of stability of official data and the reduction of the level of uncertainty. It turns out that the number of deaths is rising with the Human Development Index. It is revealed that COVID-19 divides the global population into three groups according to the relationship between Gross Domestic Product and the number of infected people. The prognosis for the number of infected people in December 2020 and January-February 2021 shows negative events which will decrease slowly. |
| Keywords | Data Science; Covid-19 |
| Year | 2021 |
| Journal | Safety Science |
| Journal citation | 136, p. 105164 |
| Publisher | Elsevier BV |
| ISSN | 0925-7535 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ssci.2021.105164 |
| Web address (URL) | http://hdl.handle.net/10545/625567 |
| https://www.elsevier.com/tdm/userlicense/1.0/ | |
| http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| hdl:10545/625567 | |
| Publication dates | 11 Jan 2021 |
| Publication process dates | |
| Deposited | 01 Feb 2021, 09:53 |
| Accepted | 05 Jan 2021 |
| Rights | © 2021 Elsevier Ltd. All rights reserved. |
Attribution-NonCommercial-NoDerivatives 4.0 International | |
| Contributors | University of Athens, Greece, Kotelnikov’s Institute of Radioengineering and Electronics, Russian Academy of Sciences, University of Mining and Technology, Xuzhou, China and University of Derby |
| File | File Access Level Open |
| File | File Access Level Open |
| File |
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