COVID-19 mortality rate in Russian regions: forecasts and reality

Abstract

Relevance. COVID-19 is an extremely dangerous disease that not only spreads quickly, but is also characterized by a high mortality rate. Therefore, prediction of the number of deaths from the new coronavirus is an urgent task. Research objective. The aim of the study is to provide a more accurate estimate of the real number of coronavirus-related deaths in Russian regions. Data and methods. The main research method is econometric modeling. Comparison of various data was also applied. The authors’ calculations were based on Rosstat data, the data of the World Bank and specialized sites with coronavirus statistics in Russia and in the world. Results. We identified the factors affecting the COVID-19 mortality rates in various countries were identified, assessed how much the official Russian statistics underestimated mortality in Russian regions, and provided predictive estimates of mortality as a result of the pandemic. We also determined the number of additional coronavirus-induced deaths. Conclusions. The official data on COVID-19 mortality in Russia underestimate the actual numbers more than twofold. The number of direct and indirect victims of the pandemic in Russia at the end of July was approximately 43 thousand people.

Stress, anxiety, depression and burnout in frontline healthcare workers during two peaks of COVID-19 pandemic in Russia

“…The level of anxiety in Russia was higher when compared with other countries ( Lai et al, 2020 ; Lu et al, 2020 ; Wanigasooriya et al, 2021 ; Zhu et al, 2020 ). This at least partially can be explained by higher contamination and mortality rates among HCWs in Russia ( Lifshits and Neklyudova, 2020 ). Another possible reason is that all participants were directly involved in treating patients with COVID-19 and worked as frontline personnel.…”

“…This result can be explained by having better working conditions, including sufficient PPE, higher salaries and full personnel strength in big cities compared to others. Mortality rates of HCWs in Russia were higher in cities other than Moscow ( Lifshits and Neklyudova, 2020 ). Some studies from other countries also confirmed that working outside of the capitals was associated with higher levels of stress and anxiety ( Gilleen et al, 2020 ; Luceño-Moreno et al, 2020 ).…”

The COVID-19 basic reproductive ratio using SEIR model for the Middle East countries and some other countries for two stages of the disease

“…Some models take the mortality into accounts such as the SIRD model (Susceptible cases of the pandemic individual, Infectious cases of the pandemic individual, Recovered cases of the pandemic individual and Deceased cases of the pandemic individual), and others take the vaccines into accounts such as the SIRV model (Susceptible cases of the pandemic individual, Infectious cases of the pandemic individual, Recovered cases of the pandemic individual and Vaccinated cases of the pandemic individual). The first derivation of epidemiologic models was by Kermack ( 1927 ), Kamara et al ( 2020 ) found analytical solution for post death transmission model for Ebola epidemics, Khan et al ( 2020 ) discussed the effects of underlying morbidities on the occurrence of deaths for the new coronavirus disease patients, Lifshits and Neklyudova ( 2020 ) discussed mortality rate in Russian regions for the new coronavirus disease, Adedire and Ndam ( 2021 ) applied a model of dual latency compartments for the transmission dynamics of the new coronavirus disease in Nigeria, Neto et al ( 2020 ) discussed the effect of the new coronavirus disease with the fourth industrial revolution, Osemwinyen and Diakhaby ( 2015 ) discussed the transmission of Ebola disease with the models, Rejaur-Rahman et al ( 2020 ) applied geospatial modelling for the new coronavirus disease, Akanda and Ahmed ( 2020 ) discussed the controlling of the disease in Bangladesh regions, Roy et al ( 2020 ) discussed the spatial predication using ARIMA (autoregressive integrated moving average) model, Santosh ( 2020 ) discussed the predication models for the new coronavirus disease with unexploited data, Kadi and Khelfaoui ( 2020 ) and Lounis and Al-Raeei ( 2021 ) applied the models for the spreading of the new coronavirus disease for Algeria, Aabed and Lashin Maha ( 2020 ) applied analytical study of the factors that influence the new coronavirus disease spread, Ali et al ( 2020 ) discussed the linkage between PM 2.5 levels and the new coronavirus disease spread and its implications for socioeconomic circles, Al-Raeei ( 2020a , b , 2021 ) found the indicators of the new coronavirus disease for different location countries over the worldwide, Bhadra et al ( 2020 ) discussed the spreading of the new coronavirus disease with mortality in Indian regions, Fang et al ( 2020 ) applied ARIMA model for Russian regions, Gao et al ( 2007 ) applied SIR model with pulse vaccination and distributed time delay, Gupta et al ( 2020 ) discussed the effects of geographical factors to the new coronavirus disease outbreak in India, Zhu et al ( 2019 ) investigated the spreading process of the epidemics on multiplex networks by incorporating fatal properties, and Aidoo et al ( 2021 ) discussed the effects of the weather on the spreading of the new coronavirus disease in Ghana. In th...…”

Natural population movement and COVID-19: data from Russia

“…According to Lifshits and Neklyudova (2020) real indicators began to be underestimated in May 2020, both in the number of cases and in the number of deaths. Kobak (2021) argues that data on additional deaths in Russia in 2020 paint a much darker picture of the death toll from Covid-19 than the official daily updated figures.…”