In order to implement the provisions of part 3 of article 18 of the federal law "On the protection of the health of citizens from exposure to surrounding tobacco smoke and the consequences of tobacco consumption" and on the basis of part 3.1 of clause 1 of article 5 of the federal law "On the basis of state regulation of trading activities in the Russian Federation" The Russian government on n s t a n o l i e in t: 1. To approve the enclosed Rules for marking tobacco products with identification means. 2. Establish that manufacturers and importers of tobacco products : a) carried out in the state information system of monitoring the circulation of goods subject to mandatory labeling by means of identification (hereinafter referred to as the information monitoring system): your registration; registration of tobacco products subject to mandatory labeling by means of identification; in the period from March 1, 2019 to June 30, 2019 (inclusive) or after June 30, 2019 within 7 calendar days from the time the manufacturer or importer needed to engage in commissioning and (or) turnover of tobacco products; Seite 1 von 33

The Government of the Russian Federation

Background Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. Objective This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. Methods We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. Results Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). Conclusions Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

In December of 2019, the first outbreak of COVID-19 was detected in mainland China, eventually spreading to every continent in the world except Antarctica. Named Coronavirus Disease 19 (COVID-19) b...

https://www.tandfonline.com/doi/pdf/10.1080/00194506.2020.1855085

Role of data science in managing COVID-19 pandemic

Relevance. The ongoing COVID-19 pandemic in the world, which is characterized by a long undulating course, requires an in-depth study of the features of the epidemic process, including the influence of natural, climatic and social factors on it. Aim. Compare the intensity of three waves of the COVID-19 pandemic in Russia. To identify the features of the parameters of the COVID-19 pandemic in Russia in the age groups of the population and in the federal districts. Materials and methods. Data from the computer database of the Influenza Research Institute and the Stop-coronavirus website were used. Results. The construction of the weekly dynamics of COVID-19 made it possible to clarify the start, peak and end dates of each wave in megacities, federal districts and among the population of the Russia. Conclusion. In the dynamics of the incidence of COVID-19 in the population of the Russian Federation from March 2020 to September 2021, three waves were detected: I spring-summer wave, II autumn-winter, III spring -summer. All three waves started in megacities, first in Moscow, and spread across federal districts. The rise of morbidity in Russia as a whole began and peaked in the autumn-winter wave later than in the spring-summer waves (immediately after the megacities). The total duration of the epidemic and the period of its development in the autumn-winter wave were longer than in the spring-summer waves. Morbidity, hospitalization and mortality depended on age, and in all three waves were higher among people over 65 years of age. The intensity of COVID-19 in the first spring-summer wave was the lowest. The II autumn-winter wave was the most intense in terms of morbidity, hospitalization rate and mortality in all age groups. The III spring-summer wave in terms of morbidity and hospitalization was less intensive, than the II autumn-winter wave, but there were no significant differences between the mortality rates in the II and III wave hospitalization and fatal outcomes were revealed. 

Comparison of the First Three Waves of the COVID-19 Pandemic in Russia in 2020–21

The aim of the study was to modernize the existing prognostic regression models in the context of expanding knowledge about the new coronavirus infection. Materials and methods The modification of models and the increase in their predictive ability are based on collecting the available data from international and Russian databases. We calculated the traditional descriptive statistics and used the linear regression analysis for modeling. The work was performed using the IBM SPSS Statistics 26.0 and the R 3.6.0 (RStudio) software. Results Manifestations of the COVID-19 epidemic process in several countries were studied; special attention was put to the number of deaths associated with the infection. A significant proportion of severe cases were noted among patients both in Russia and elsewhere. Considering that the disease incidence has reached its peak in China and Italy, we were able to improve the previously published (Sovremennye tehnologii v medicine 2020, Vol. 12, No.2) regression models and to compare their performance. The first modified model is based on the absolute increase in new cases of the infection: its regression coefficient is 0.16 (95% CI 0.137-0.181). In the extended version of the updated model, we additionally considered cases of aggravated COVID-19: the regression coefficients were 0.128 (95% CI 0.103-0.153) for model 2 and 0.053 (95% CI 0.029-0.077) for model 1.1; p=0.0001. Conclusion Based on the most recent data (from January to May 2020) on the incidence of COVID-19 in the world, we have developed more specific versions of the basic and extended regression models of lethal outcomes. The resulting models are optimized and extrapolated to the current epidemiological situation; they will allow us to improve our analytical approach. For that purpose, data collection is currently ongoing.

/pdf/modernization-of-regression-models-to-predict-the-number-of-31zqxvt0s7.pdf

Modernization of regression models to predict the number of deaths from the new coronavirus infection

Predicting the development of epidemic infection caused by the COVID-19 coronavirus is a matter of the utmost urgency for health care and effective anti-epidemic measures. Given the rapidly changing initial information and the ambiguous quality of data coming from various sources, it is important to quickly optimize the existing prognostic models by using more sophisticated algorithms. The aim of the study is to test the originally developed mathematical algorithms for predicting the development of the COVID-19 epidemic process. Materials and Methods. To assess the situation in China, Italy, and the USA, we used the information from Russian- and Englishlanguage sources available in official websites. The generally accepted descriptive statistics were used; mathematical modeling was based on linear regression. Statistical data processing was performed using the IBM SPSS Statistics 24.0 and R (RStudio) 3.6.0. Results. We found significant differences not only in the incidence rate of COVID-19 in the countries in question, but also in the death rate. The risk of death associated with COVID-19 is high due to the high number of severe clinical cases of the disease reported from these countries. Two preliminary regression models were created. The first, initial model was based on the increase in new cases of infection — this factor was significantly associated with the outcome; the regression coefficient was 0.02 (95% CI 0.01–0.03). In the second, expanded model, in addition to the increase in new cases, the increase in the number of severe forms of infection was also considered; the regression coefficients were 0.017 (95% CI 0.012–0.022) and 0.01 (95% CI 0.008–0.011), respectively. Adding the second variable contributed to a more accurate description of the available data by the model. Conclusion. The developed regression models for infection control and predicting the number of lethal outcomes can be successfully used under conditions of spreading diseases from the group of “new infections” when primary data received from various sourced are changing rapidly and updates of the information are continually required. In addition, our initial model can produce a preliminary assessment of the situation, and the expanded model can increase the accuracy and improve the analytic algorithm.

/pdf/regression-models-predicting-the-number-of-deaths-from-the-ledaq8bbv0.pdf

Regression models predicting the number of deaths from the new coronavirus infection

Relevance. The COVID-19 pandemic is characterized by a long undulating course. One of the directions of the dynamic assessment of the incidence of this infection is, as is known, the characterization of the determinants of the epidemic process and the study of the actual effectiveness of various measures. Aims. Were to study the features of the COVID-19 morbidity in the European, American and Asian regions of the world on the example of individual countries with an assessment of the possible impact of regime-restrictive measures on the daily increase in cases. Materials & methods. A descriptive epidemiological study involved the use of the following data on COVID-19: daily increase in new infections in absolute numbers and relative indicators during 1 June 2020 till 30 November 2020 in five countries (France, Italy, USA, Brazil, India) , description and timing of various restrictive measures. Information obtained from open sources (situation reports from WHO, CDC, ECDC, national ministries of health, etc.). Time series characterized, defining sharply differing values, timing and duration of ups and downs, the rate of average daily growth (decline). Statistical analysis was carried out using the IBM SPSS Statistics 26. Results. On average, for the analyzed period of time, 1303 were registered in Italy, 4897, France – 52799, Brazil – 31853, India –50507new cases. The average incidence rate in the compared countries ranged from 500.98 ± 417.06 per 100,000 in India to 4399.43 ± 2390.77 per 100,000 in the US. After the passage of the «first wave» of the incidence of COVID-19, regardless of the region of the world, there was an increase in the daily increase in new cases of SARS-CoV-2 in the summer-autumn period of 2020. Furthermore, with the differences in the morbidity rates in the different countries, there were also characteristics the formation of similar to the region. For the European region (Italy, France), there was a simultaneous beginning of an increase in the incidence in August-September 2020, a similar trend towards exponential growth and synchronous fluctuations in the daily increase in absolute cases of diseases. For the countries of the American region (USA and Brazil), a similar sinusoidal nature of the dynamics of the average daily increase in infection cases and its synchronicity until October 2020 was revealed. The Asian region, on the example of India, had significant differences in the dynamics of the analyzed indicators in comparison with the countries of the European and American regions. Differences in the formation of morbidity in the summerautumn period were more pronounced between the regions and related to the level of average daily growth, the incidence rate, the month of the maximum rise in the incidence in this period, and trend differences. Comparison of the ongoing isolation measures with the daily increase in cases revealed their discrepancy. This could create the preconditions for the activation of the epidemic process of infection and the ineffectiveness of measures. Conclusions. We found that in the five countries examined, the situation developed according to a similar scenario. Nevertheless, in different regions of the world there was a specificity in the involvement of the territory in the epidemic process. A more in-depth study of the timeliness and completeness of regime-restrictive measures against SOCID-19 should include a comparison with the patterns of formation and manifestations of the epidemic process. In turn, this is important for scientifically based implementation and increasing their effectiveness.

/pdf/comparative-characteristics-of-a-subsequent-morbidity-wave-1a6nsfm2eh.pdf

O. V. Drugova

Papers

Regression models predicting the number of deaths from the new coronavirus infection

Comparative characteristics of a subsequent morbidity wave COVID-19 in various regions of the world

Modernization of regression models to predict the number of deaths from the new coronavirus infection