COVID-19 mortality rate in Russia: forecasts and reality evaluation
Lifshits, M.; Neklyudova, N.
Show abstract
COVID-19 is an extremely dangerous disease that not only spreads quickly, but is also characterized by a high mortality rate. Therefore, predicting the number of deaths from the new coronavirus is an urgent task. The aim of the study is to analyze the factors affecting COVID-19 mortality rate in various countries, to predict direct and indirect victims of the pandemic in the Russian Federation, and to estimate additional mortality during the pandemic based on the demographic data. The main research method is econometric modeling. Comparison of various data was also applied. The authors' calculations were based on data from the RSSS, the World Bank, as well as specialized sites with coronavirus statistics in Russia and in the world. A predictive estimation of the deceased number of people due to the pandemic in Russia was made. It is confirmed that the deaths proportion of the completed cases of the disease depends on the level of testing. It is shown that the revealed mortality of the disease depends on the proportion of completed cases, on the population age structure, and on how early the pandemic entered the country compared to the other countries. It is determined that the number of additional deaths due to the coronavirus is approximately 31 thousand people. The analysis revealed that the relatively low proportion of COVID in Russia is the result of a special approach to the cause of death determination. The mortality rate in Russia in April 2020 was about 3% higher than in April 2019. The share of the deceased health workers in the total coronavirus mortality in the Russian Federation is higher than in the developed countries, which indicates an underestimation of the data on COVID- 19 deaths in the Russian Federation, and the unsatisfactory quality of the Russian healthcare system. The number of direct and indirect victims of the pandemic in the Russian Federation at the end of July was approximately 43 thousand people.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interdependence between confirmed and discarded cases of dengue, chikungunya and Zika viruses in Brazil: A multivariate time-series analysis 95%
- Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques: A comparative study 95%
- Prediction and control of COVID-19 infection based on a hybrid intelligent model 94%
Similar papers in this journal
- Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19) 94%
- Using A Socio-Ecological System (SES) Framework to Explain Factors Influencing Countries’ Success Level in Curbing COVID-19 93%
- Quantifying the Effects of Social Distancing on the Spread of COVID-19 93%
Similar papers in this journal
Similar papers in this journal
- Estimating the risk of COVID-19 death during the course of the outbreak in Korea, February- May, 2020 93%
- Risk assessment of novel coronavirus COVID-19outbreaks outside China 92%
- Epidemiological identification of a novel infectious disease in real time: Analysis of the atypical pneumonia outbreak in Wuhan, China, 2019-20 91%
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 94%
- Mathematical modelling of dynamics and containment of COVID-19 in Ukraine 94%
- Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.