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Title Why do regions differ in vulnerability to сovid-19? Spatial nonlinear modeling of social and economic patterns
Authors Kuzmenko, Olha Vitaliivna  
Vasylieva, Tetiana Anatoliivna  
Chygryn, Olena Yuriivna  
Vojtovič, Sergej
Snieška, Vytautas
ORCID http://orcid.org/0000-0001-8575-5725
http://orcid.org/0000-0003-0635-7978
http://orcid.org/0000-0002-4007-3728
Keywords COVID-19
vulnerability
modelling
public health
вразливість
уязвимость
моделювання
моделирование
громадське здоров'я
здравоохранение
Type Article
Date of Issue 2020
URI https://essuir.sumdu.edu.ua/handle/123456789/85698
Publisher Economics and sociology
License
Citation Kuzmenko O., Vasylieva T., Vojtovič S., Chygryn O., & Snieška V. (2020). Why do regions differ in vulnerability to СOVID-19? Spatial nonlinear modeling of social and economic patterns. Economics and Sociology, 13(4), 318-340. doi:10.14254/2071-789X.2020/13-4/20
Abstract Certain groups of determinants (economic, environmental, social, healthcare) with the highest vulnerability identify the reasons for regional differentiation in morbidity and mortality from COVID-19. This defines the necessity to find appropriate combinations of factors characterizing the vulnerability of a region. The methodology and tools to explain the regional specifics of population vulnerability to COVID-19 are investigated through a systematic consideration of many public health factors, environmental, social and economic specific nature of regions. The aim of the article is to study the reasons for regional differentiation of population vulnerability (morbidity and mortality rates) from COVID-19. The authors investigate a nonlinear spatial model in which the stepwise algorithm of individual factor variables is added/removed from the model specifications step by step by the Aitken method depending on their correlation with morbidity and mortality from COVID-19 in the region. The Farrar-Glober method is used to eliminate the multicollinearity of factors, the Spearman test is used to detect the heteroskedastic effect, and the Darbin-Watson test is used to check the presence of autocorrelation between the residues. As a result, the specification of the model with the highest adequacy in terms of p-value and t-statistics is formed. Relevant socioecological-economic vulnerability indices of regions to mortality and morbidity from COVID-19 are identified. The obtained results allow making adjustments in the state and regional programs concerning the mobilization of economic and healthcare systems.
Appears in Collections: Наукові видання (ННІ БіЕМ)

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