COMPARATIVE ANALYSIS OF THE FEATURES OF ORGANIZING MEASURES TO COMBAT THE COVID-19 PANDEMIC IN THE HEALTH SYSTEMS OF THE RUSSIAN FEDERATION AND FOREIGN COUNTRIES

The study analyzed available literatures covering the organization of measures to combat the COVID-19 pandemic in the healthcare systems of the Russian Federation and several foreign countries. For the comprehensive assessment of the specifics of organizing measures to combat the COVID-19 pandemic, countries were chosen based on geographical distances from China (the closest is Korea, and the most remote are the Great Britain and Haiti), maximum population on their continent (the United States is in North America, and Brazil in South America), and significant differences in the functioning of the healthcare systems. The peculiarities of organizing measures to combat the COVID-19 pandemic in the considered countries were associated with a complex of political, financial, economic, demographic, and organizational factors, the individual combination of which determined the peculiarities of the development of the epidemic process in each specific case. Moreover, as a priority manifestation of the severity of these factors, the capabilities of the healthcare system, including the availability of services of medical workers, sufficient number of testing equipment, medical protection equipment, hospital beds, and other parameters, should be considered. The main role was played by global state strategies implemented in the healthcare systems of the analyzed countries at the pre-epidemic stage and, in most cases, aimed at optimizing the financial and economic provisions of state guarantees of medical care. The general criteria for the differential diagnosis of COVID-19 in the national recommendations of all the states considered were respiratory symptoms and general infectious intoxication. In addition, fever and respiratory symptoms were accepted as priority criteria for COVID-19 screening. © 2023 Vestnik Rossijskoj Voenno-Medicinskoj Akademii. All rights reserved.

ASSESSING THE IMPACT OF THE RUSSIAN WAR IN UKRAINE ON COVID-19 TRANSMISSION IN SPAIN: A MACHINE LEARNING-BASED STUDY

COVID-19 pandemic has significantly impacted the world, with millions of infections and deaths, healthcare systems overwhelmed, economies disrupted, and daily life changed. Simulation has been recognized as a valuable tool in combating the pandemic, helping to model the spread of the virus, evaluate the impact of interventions, and inform decision-making processes. The accuracy and effectiveness of simulations depend on the quality of the underlying data, assumptions, and modeling techniques. Ongoing efforts to improve and refine simulation approaches can enhance their value in addressing future public health emergencies. The Russian full-scale mil-itary invasion of Ukraine on February 24, 2022, has created a significant humanitarian and public health crisis, with disrupted healthcare services, shortages of medical supplies, and increased demand for emergency care. The ongoing conflict has displaced millions of people, with Spain ranking 5th in the world for the number of registered refugees from Ukraine. The research aims to estimate the impact of the Russian war in Ukraine on COVID-19 transmission in Spain using means of machine learning. The research is targeted at COVID-19 epi-demic process during the war. The research subjects are methods and models of epidemic process simulation based on machine learning. To achieve the study's aim, we used forecasting methods and built a model of COVID-19 epidemic process based on the XGBoost method. As a result of the experiments, the accuracy of forecasting new cases of COVID-19 in Spain for 30 days was 99.79 %, and the death cases of COVID-19 in Spain – were 99.86 %. The model was applied to data on the incidence of COVID-19 in Spain for the first 30 days of the war escalation (24.02.2022 – 25.03.2022). The calculated forecasted values showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain. Conclusions. The paper describes the results of an experimental study assessing the impact of the Russian full-scale war in Ukraine on COVID-19 dynamics in Spain. The developed model showed good performance to use it in public health practice. The anal-ysis of the obtained results of the experimental study showed that the forced migration of the Ukrainian popula-tion to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain © Dmytro Chumachenko, Tetiana Dudkina, Tetyana Chumachenko, 2023

Forecasting of COVID-19 Dynamics by Agent-Based Model

The novel coronavirus pandemic has continued to spread worldwide for more than two years. The development of automated solutions to support decision-making in pandemic control is still an ongoing challenge. This study aims to develop an agent-based model of the COVID-19 epidemic process to predict its dynamics in a specific area. The model shows sufficient accuracy for decision-making by public health authorities. At the same time, the advantage of the model is that it allows taking into account the stochastic nature of the epidemic process and the heterogeneity of the studied population. At the same time, the adequacy of the model can be improved with a more detailed description of the population and external factors that can affect the dynamics of the epidemic process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Quarantine and Vaccination in Hierarchical Epidemic Model

The analysis of global epidemics, such as SARS, MERS, and COVID-19, suggests a hierarchical structure of the epidemic process. The pandemic wave starts locally and accelerates through human-to-human interactions, eventually spreading globally after achieving an efficient and sustained transmission. In this paper, we propose a hierarchical model for the virus spread that divides the spreading process into three levels: a city, a region, and a country. We define the virus spread at each level using a modified susceptible–exposed–infected–recovery–dead (SEIRD) model, which assumes migration between levels. Our proposed controlled hierarchical epidemic model incorporates quarantine and vaccination as complementary optimal control strategies. We analyze the balance between the cost of the active virus spread and the implementation of appropriate quarantine measures. Furthermore, we differentiate the levels of the hierarchy by their contribution to the cost of controlling the epidemic. Finally, we present a series of numerical experiments to support the theoretical results obtained. © 2023 by the authors.

Features of the epidemic process and epidemic risks of COVID-19 in the subjects of the Northern Caucasus

Relevance. The peculiarities of the course of the COVID-19 epidemic process in the regions of the world, as a rule, are determined by the epidemic risks characteristic of them. Identification and evaluation of the latter is necessary to improve measures to counter infection in a particular area. Aim. To study the features of the COVID-19 epidemic process in the regions of the North Caucasus, to identify and assess the impact of epidemic risk factors on the epidemic situation. Materials and methods. The data of the Departments of Rospotrebnadzor for the subjects of the North Caucasus, Internet resources: stopkoronavirus were used Russia, Johns Hopkins University and Our World in Data project. Statistical processing was carried out using methods of variation statistics and MS Excel software package (2016, USA). Correlation analysis was performed using Spearman's coefficient. Results. As of 01.12.2021, the incidence of COVID-19 in the Caucasus was lower than the Russian average (3890.0 and 65921.8 per 100 thousand population), and the mortality rate was higher (4.8 and 1.9%, respectively). The dynamics of the epidemic process as a whole repeated the situation in the Russian Federation, but with a delay of each phase by 2–3 weeks and had 4 periods of rising morbidity (waves), with the exception of the Stavropol Territory, the Republics of Adygea and Ingushetia, where three waves of morbidity were observed. In the Republics of Dagestan, Chechen and Adygea, a high proportion of community-acquired pneumonia was noted-58.8, 47.0 and 34.1%, respectively. The mortality rate from a new coronavirus infection was higher than the national average in Dagestan (in the period of the 1st wave – 4.7%), Krasnodar Krai (in the period of the 2nd wave – 5.0%;3 – 12.6%, in 4 – 9.9%), Karachay-Cherkess Republic (in the 3rd period of the rise – 9.0%) and in Stavropol Krai (in 4 – 7.6%). The lowest mortality from COVID-19 in the 3rd and 4th periods of the rise in morbidity in the North Caucasus was registered in Ingushetia – 2.2 and 2.1%, respectively. The exceptional situation in the Republic of Dagestan is due not only to a large proportion of community-acquired pneumonia and high mortality from COVID-19, compared with other regions of the North Caucasus and Russia as a whole, but also to an increase in excess mortality in the republic. The excess of the average annual (over the previous 5 years) number of deaths in the Caucasus in 2020 was +19.8%, in 2021 +32.7%, in the Russian Federation as a whole 14.8 and 31.4%, respectively. Conclusion. The general epidemic risks of COVID-19 for the North Caucasus region have been identified – the level of vaccination, the implementation of non–specific prevention measures, as well as local risks for specific regions: in Dagestan – adherence to local mass ceremonies, as well as insufficient control over the implementation of restrictive measures, in Ingushetia – relatively high population density. © 2023, Numikom. All rights reserved.

Simulation of Impact of Migration Caused by the War in Ukraine on COVID-19 Epidemic Process in Moldova

The novel coronavirus pandemic has become a global challenge and has shown that health systems worldwide are unprepared for pandemics of this magnitude. The war in Ukraine, escalated by Russia on February 24, 2022, brought deaths and a humanitarian catastrophe and stimulated the spread of COVID-19. Most refugees who evacuated from the war crossed the border with other countries. At the end of July, almost 550 thousand people crossed the border with Moldova. This study is devoted to modeling the impact of migration processes on the dynamics of COVID-19 in Moldova. For this, a machine learning model was built based on the polynomial regression method. The forecast accuracy a month before the escalation of the war was from 98.77% to 96.37% for new cases and from 99.8% to 99.75% for fatal cases. The forecast accuracy for the first month after the escalation of the war was from 99.96% to 99.34% for new cases and from 99.91% to 99.88% for fatal cases. The high accuracy of the model, both before the war and with the start of its escalation, suggests that the migration flows of refugees from Ukraine to Moldova did not affect the dynamics of COVID-19. ©2022 Copyright for this paper by its authors.

PREDICTIVE MODEL OF COVID-19 EPIDEMIC PROCESS BASED ON NEURAL NETWORK

The COVID-19 pandemic, which has been going on for almost three years, has shown that public health systems are not ready for such a challenge. Measures taken by governments in the healthcare sector in the context of a sharp increase in the pressure on it include containment of the transmission and spread of the virus, providing sufficient space for medical care, ensuring the availability of testing facilities and medical care, and mobilizing and retraining medical personnel. The pandemic has changed government and business processes, digitalizing the economy and healthcare. Global challenges have stimulated data-driven medicine research. Forecasting the epidemic process of infectious processes would make it possible to assess the scale of the impending pandemic to plan the necessary control measures. The study builds a model of the COVID-19 epidemic process to predict its dynamics based on neural networks. The target of the research is the infectious diseases epidemic process in the example of COVID-19. The research subjects are the methods and models of epidemic process simulation based on neural networks. As a result of this research, a simulation model of COVID-19 epidemic process based on a neural network was built. The model showed high accuracy: from 93.11% to 93.96% for Germany, from 95.53% to 95.54% for Japan, from 97.49% to 98.43% for South Korea, from 93.34% up to 94.18% for Ukraine, depending on the forecasting period. The assessment of absolute errors confirms that the model can be used in healthcare practice to develop control measures to contain the COVID-19 pandemic. The respective contribution of this research is two-fold. Firstly, the development of models based on the neural network approach will allow estimate the accuracy of such methods applied to the simulation of the COVID-19 epidemic process. Secondly, an investigation of the experimental study with a developed model applied to data from four countries will con-tribute to empirical evaluation of the effectiveness of its application not only to COVID-19 but also to other infectious diseases simulations. Conclusions. The research's significance lies in the fact that automated decision support systems for epidemiologists and other public health workers can improve the efficiency of making anti-epidemic decisions. This study is especially relevant in the context of the escalation of the Russian war in Ukraine when the healthcare system's resources are limited. © Serhii Krivtsov, Ievgen Meniailov, Kseniia Bazilevych, Dmytro Chumachenko, 2022

Modifying impact of environmental factors on the course of an epidemic process

Introduction. It is necessary to establish peculiarities and regularities of COVID-19 infection;this task requires further research on how to formalize and build spatial-temporal models of the infection spread. This article focuses on determining non-infectious factors that can modify the epidemic process caused by the COVID-19 coronavirus for further substantiation of integrated solutions that are necessary to ensure sanitary-epidemiological welfare of the RF population. Materials and methods. Our study involved analyzing regularities of regional differentiation in parameters introduced into mathematical models. These models described how the epidemic process developed in RF regions depending on modifying non-infectious factors identified by modelling the dynamics of spread of SARS-CoV-2 delta strain. These modifying factors included anti-epidemic activities;sanitary-epidemiological, sociodemographic, and economic conditions in a region;weather and climate;public healthcare systems and people's lifestyles in RF regions over 2020–2021. The dynamics of the epidemic process was modelled by using the conventional SIR-model. Relationships between parameters introduced into the model of the epidemic process and modifying regional conditions were examined by using correlation-regression analysis. Results. The modelling made it possible to identify priority risk factors that modified COVID-19 spread authentically (p<0.05) and explained regional differences in intensity of contagion, recovery and lethality. We established that population coverage with vaccination, especially among people aged 31–40 years, had the greatest authentic positive influence on the decline of reproduction index (R0) of the virus (r=–0.37). An increase in monthly average temperatures in autumn and winter as well as over a year made for people moving faster from the susceptible to infected category (r=0.21–0.22). Growing sun insolation over a year, especially in summer, resulted in slower movement of susceptible people into the infected category (r=–0.02–(–0.23)). Next, several sanitary-epidemiological indicators authentically made the infection spread faster;they were improper working conditions (not conforming to the safety standards as per physical indicators) and ambient air quality in settlement not corresponding to the hygienic standards as per chemical indicators and noise (r=0.29–0.24). Recovery took longer in regions where alcohol consumption was comparatively higher (r=–0.32). Limitations. The limitations of the study include modelling the epidemic process using the standard SIR model;limited set of indicators and period of analysis. Conclusions. The existing regional differentiation in development of specific stages in the epidemic process related to the COVID-19 delta strain occurs due to complex interactions and influence exerted by modifying factors that create a certain multi-level and multi-component system. This system is able to transform the epidemic process either potentiating it or slowing it down. © 2022 Izdatel'stvo Meditsina. All rights reserved.

Impact of Migration on COVID-19 Epidemic Process in Romania: A Simulation Study

The Russian war in Ukraine, which escalated on February 24, 2022, caused massive destruction and the death of thousands of people. In addition, the Russian invasion has affected the public health system and the spread of infectious diseases. Millions of Ukrainians fled from the war, which caused a pan-European migration crisis. This study is devoted to testing the hypothesis of the impact of population migration caused by the Russian war in Ukraine on the dynamics of the spread of COVID-19 in Romania. For this, a machine learning model was developed based on the polynomial regression method. The model showed high accuracy. However, the formulated hypothesis was not confirmed fully. The results of the experimental study showed that population migration have not impacted the fatality caused by COVID-19, but has the impact on COVID-19 new cases. The further investigation is needed to find out the exact factors which influenced the epidemic process. © 2022 IEEE.

Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning

The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content. © 2022 Copyright for this paper by its authors.

Contact tracing & super-spreaders in the branching-process model.

In recent years, it became clear that super-spreader events play an important role, particularly in the spread of airborne infections. We investigate a novel model for super-spreader events, not based on a heterogeneous contact graph but on a random contact rate: Many individuals become infected synchronously in single contact events. We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing. Here we neglect a tracing delay. Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period. In other cases, the effect of contact tracing can be decreased by super-spreaders. Numerical analysis with parameters suited for SARS-CoV-2 indicates that super-spreaders do not decrease the effect of contact tracing crucially in case of that infection.

COVID-19 in Russia: Epidemiology and Molecular Genetic Monitoring

Background. The COVID-19 pandemic has exposed health problems, but at the same time has become a powerful impetus for the development of new scientific research in the field of epidemiology, clinic of infectious diseases, diagnostics, bioinformatics and digital methods. On the basis of the Central Research Institute of Epidemiology of Rospotrebnadzor, new unique test systems for the detection of SARS-CoV-2 RNA based on real-time PCR based on loop isothermal amplification (IT) technology have been developed, which allows you to examine samples 3-4 times faster than those developed previously. The Central Research Institute of Epidemiology of Rospotrebnadzor has developed and put into operation the Russian Platform for Aggregation of Information on Virus Genomes (VGARus). The VGARus database contains information about the nucleotide sequences of the SARS-CoV-2 viruses and their mutations, and the SOLAR Integration Platform has been created to quickly transfer the results of PCR studies to all interested citizens of the Russian Federation. Aims - to study the manifestations of the epidemic process of COVID-19 and the prevalence of genovariants of the SARS-CoV-2 virus in the Russian Federation. Methods. A retrospective epidemiological analysis of the incidence of COVID-19 was carried out from March 30, 2020 to May 17, 2022 in the Russian Federation. The database was formed on the basis of the materials of the Rospotrebnadzor report form No. 970 "Information on cases of infectious diseases in persons with suspected new coronavirus infection", data from the WHO, the domestic information portal Stopkoronavirus.rf and the Yandex DataLens data visualization and analysis service were used, information on SARS-CoV-2 genovariants in the VGARus database. Results. When analyzing the manifestations of the COVID-19 epidemic process in the Russian Federation for 2020-2022. Two stages have been identified. The first stage was characterized by the rapid spread of the SARS-CoV-2 virus from megacities to other regions of the country and the use of non-specific prevention measures. The beginning of the second was due to the evolution of the SARS-CoV-2 virus and a change in its biological properties, followed by a change in the prevailing genovariants on the territory of the Russian Federation. Conclusion. As a result of the study, it was found that with each subsequent increase in the incidence of COVID-19, there was a decrease in the severity of the course and the proportion of pneumonia in the structure of the clinical forms of the disease. Copyright © 2022 Izdatel'stvo Meditsina. All rights reserved.

BARRIERS OF COVID-19 VACCINATION IN UKRAINE DURING THE WAR: THE SIMULATION STUDY USING ARIMA MODEL

The COVID-19 pandemic has become a challenge to public health systems worldwide. As of June 2022, more than 545 million cases have been registered worldwide, more than 6.34 million of which have died. The gratui-tous and bloody war launched by Russia in Ukraine has affected the public health system, including disruptions to COVID-19 vaccination plans. The use of simulation models to estimate the necessary coverage of COVID-19 vaccination in Ukraine will make it possible to rapidly change the policy to combat the pandemic in the wartime. This study aims to develop a COVID-19 vaccination model in Ukraine and to study the impact of war on this process. The study is multidisciplinary and includes a sociological study of the attitude of the population of Ukraine toward COVID-19 vaccination before the escalation of the war, the modeling of the vaccine campaign, forecasting the required number of doses administered after the start of the war, epidemiological analysis of the simulation results. This research targeted the COVID-19 epidemic process during the war. The research sub-jects are the methods and models of epidemic process simulation based on statistical machine learning. Socio-logical analysis methods were applied to achieve this goal, and an ARIMA model was developed to assess COVID-19 vaccination coverage As a result of the study, the population of Ukraine was clustered in attitude to COVID-19 vaccination. As a result of a sociological study of 437 donors and 797 medical workers, four classes were distinguished: supporters, loyalists, conformists, and skeptics. An ARIMA model was built to simulate the daily coverage of COVID-19 vaccinations. A retrospective forecast verified the model's accuracy for the period 01/25/22 - 02/23/22 in Ukraine. The forecast accuracy for 30 days was 98.79%. The model was applied to esti-mate the required vaccination coverage in Ukraine for the period 02/24/22 – 03/25/22. Conclusions. A multi-disciplinary study made it possible to assess the adherence of the population of Ukraine to COVID-19 vaccina-tion and develop an ARIMA model to assess the necessary COVID-19 vaccination coverage in Ukraine. The model developed is highly accurate and can be used by public health agencies to adjust vaccine policies in wartime. Given the barriers to vaccination acceptance, despite the hostilities, it is necessary to continue to per-form awareness-raising work in the media, covering not only the events of the war but also setting the population on the need to receive the first and second doses of the COVID-19 vaccine for previously unvaccinated people, and a booster dose for those who have previously received two doses of the vaccine, involving opinion leaders in such works © Dmytro Chumachenko, Tetyana Chumachenko, Nataliia Kirinovych, Ievgen Meniailov, Olena Muradyan, Olga Salun, 2022

EPIDEMIOLOGICAL CHARACTERISTICS OF A NEW CORONAVIRUS INFECTION IN THE STAVROPOL TERRITORY IN 2020

Based on the study of the COVID-19 epidemic process in the Stavropol Region in 2020, it is proposed to divide it into four periods - the <<introduction>> of infection, the beginning of the increase in morbidity, the stable level of the new COVID-19 cases number, and the second period of the morbidity increasing. The dynamics of clinical and epidemiological indicators in each period of the epidemic process is characterized. The main factors of epidemic risk are established: the high contagiosity and the rate of spread of the infectious agent, the long incubation period, active migration flows (mainly in the first period), and the formation of the infection foci (during all phases of the epidemic process). The features of the epidemic process in each periods are shown: in the II period, COVID-19 was mainly registered in the socially active citizens groups of young working age (18-49 years), in the III and IV periods, the higher level of morbidity was in people 30-64 years old;in the II and III periods, the highest level of morbidity was registered in medical workers and pensioners;in the I and II periods of the epidemic process, a significant proportion of patients had an asymptomatic infection (54.7 and 45.7%, respectively);the maximum number of individuals with an unidentified source of infection was registered mainly in the third period (34.7 %). Due to the change in diagnostic tactics against the background of an increase in morbidity in the III and IV periods, the structure of the clinical forms and severity of the course of COVID-19 has changed. The timely introduction of restrictive measures and the organization of mass examinations of citizens, including those who arrived to the territory of the region, made it possible to avoid a rapid increase in the number of COVID-19 patients in the region. Copyright © 2022 Stavropol State Medical University. All rights reserved.

Study of Influence of Public Opinion about Vaccination on COVID-19 Immunization

This study is aimed at assessing impact of public opinion about vaccination on immunization against COVID-19 and, as a result, implementation of measures for the non-proliferation of morbidity among the population. The study differs from previous ones in the following areas: 1) posts from the most popular social network in Russia, VKontakte, are analyzed for the first time;2) influence of intensity of the epidemic process and attitude of the population to vaccination on inoculation coverage is considered;3) lag effect and influence on running time of indicated factors on vaccination, are considered. Morbidity dynamics in Russia and vaccination rate were analyzed according to the portal "Our World in Data". Attitude of population to vaccination is determined through sentiment analysis of posts of Vkontakte in 2021. Assessment of dependence of people's attitude to vaccination and implementation of immunization process, as well as the spread of viral infection, is studied using Granger causality test. The results of the article can be used in solving problems of increasing effectiveness of implementation of state anti-epidemic measures and management of vaccination process. © 2022 IEEE.

HERD IMMUNITY TO SARS-CoV-2 AMONG ADOLESCENT STUDENTS OF THE RUSSIAN DEFENSE MINISTRY COLLEGES OF THE CITY OF SAINT PETERSBURG

The morbidity of the new coronavirus infection (COVID-19) in the Russian Armed Forces is decreasing thanks to the sanitary and preventive anti-epidemic measures, the most effective of which is mass vaccination. The purpose of this research was to study the peculiarities of the formation of herd immunity among adolescent students of the Russian Ministry of Defense (MoD) colleges against the background of the COVID-19 epidemic. Materials and methods of the research: according to the epidemic indications, a two-stage seroepidemiological multicenter prospective study of herd immunity to SARS-CoV-2 was carried out in Dec. 2021 - May 2022, against the background of vaccination, among adolescent students of the Russian Defense Ministry colleges. 515 adolescents aged 11 to 17 years old (median age 13 [12;15] years old) from the two Russian MoD schools located in the city of Saint Petersburg, of which 292 (57%) girls and 223 (43%) boys, were involved in the study. The adolescents were divided into groups based on gender and previous COVID-19 illness e.g., those who already had COVID-19 and those who had not prior to the study. In the second stage of the study the number of samples from boys and girls decreased by 74.3% and 34.4%, respectively, due to the lack of parents' consent to the vaccination. The assessment of the immunity intensity was carried out using the Anti-SARS-CoV-2 IgG levels in blood serum by enzyme-linked immunosorbent assay (ELISA). Result(s): the initially high levels of seroprevalence to SARS-CoV-2 were established among both girls and boys (90.4% and 91.5%, respectively, p=0.09) that indicated a latent course of the epidemic process in the studied groups of teenagers. In adolescent girls and boys vaccinated against the background of a previous COVID-19 illness, the combined immunity is formed in 62.3% and 68.1%, respectively (p=0.11). Conclusion(s): the epidemic process of COVID-19 tends to be latent in organized adolescent groups, being realized in inapparent forms of the infection. Those adolescents who've been vaccinated after COVID-19 illness develop the immunity with positive seroconversion dynamics. Copyright © 2022, Pediatria Ltd. All rights reserved.

Analysis of the Covid-19 Process in Terms of Health Managers

In this study, sentiment analysis was conducted on the data of the Covid-19 epidemic process from the official twitter account of the Republic of Turkey Fahrettin Koca, Minister of Health, @drfahrettinkoca (SO) and the Twitter account of the @WHO (World Health Organization). First of all, twitter data was obtained and necessary arrangements were made for analysis. Then, tweets were shown with a word cloud and it was determined which words were used more frequently. Afterwards, sentiment analysis was performed on the data using the TextBlob library. In addition, it has been found out which subjects are focused on tweets sent from SO and @WHO (World Health Organization) accounts with the LDA algorithm. It has been seen that positive tweets were sent from both accounts, giving positive messages to the society. © 2022 IEEE.

COVID-19: the evolution of the pandemic in Russia. Report I: manifestations of the COVID-19 epidemic process

Background. The ongoing pandemic of a new coronavirus infection (COVID-19) determines the relevance of the analysis of epidemiological patterns of SARS-CoV-2 spread among the population of the Russian Federation. Aim — study of the manifestations of the epidemic process of COVID-19 in the Russian Federation in 2020–2022. Materials and methods. A retrospective epidemiological analysis of the incidence of COVID-19 in the Russian Federation was carried out from 03/30/2020 to 04/24/2022. The data from the Rospotrebnadzor report No. 970 “Information on cases of infectious diseases in persons with suspected new coronavirus infection”, information portal Stopcoronavirus.rf, etc. were used. The presence of SARS-CoV-2 RNA was confirmed by real-time RT-PCR. Results and discussion. The analysis of the manifestations of the epidemic process of COVID-19 in the Russian Federation in 2020–2022 showed the presence of two stages which differed depending on the influence of the biological factor and the ongoing anti-epidemic measures. There was a pronounced trend in the development of the epidemic process, starting from megacities (Moscow, Moscow region and St. Petersburg), which are major transport hubs and centers of migration activity of the population, to the regions of the Russian Federation. The SARS-CoV-2 pathogenicity has been shown to decrease with each subsequent cycle of the rise in the incidence of COVID-19 against the background of the increased contagiousness of the virus. Conclusion. As a result of the study, risk areas (megacities) and risk groups were identified. © 2022, Central Research Institute for Epidemiology. All rights reserved.

NEW CORONAVIRUS PANDEMIC WORLDWIDE: SOME LESSONS FROM COVID-19 CONTROL

The aim of the study was to perform a comparative analysis of COVID-19 epidemic process in selected countries of the world during the first pandemic wave in 2020 and during the rise of SARS-CoV2 variant Omicron. Materials and research methods. Analysis of the COVID-19 epidemic process was based on data from the Wordometers website (https://www.worldometers.info/coronavirus/#countries). In addition, scientific and popular science articles and official documents on the history, epidemiology, and response to the pandemic in different countries of the world in 2020-2022 were analyzed. The authors' own observations were also used. Results of the study and their analysis. Restrictive measures adopted in the People's Republic of China (PRC), mass screening of the population, observation of those arriving in the country and hospitalization of all those infected made it possible to virtually reduce the circulation of the virus to zero. In the Russian Federation, timely simultaneous epidemic control measures throughout the country resulted in a significant decline in the intensity of the epidemic, both early in the pandemic and after local Omicron transmission, and prevented explosive growth of cases. In the USA, Italy and Sweden, untimely or lenient restrictive measures and low testing during selected periods of the pandemic led to an avalanche of cases and deaths. Thus the epidemic process of COVID1-9 in the analysed countries depended on the timeliness, duration and extent of restrictive and quarantine measures. © 2022, Ministry of Health of the Russian Federation. All rights reserved.

IMPACT OF WAR ON COVID-19 PANDEMIC IN UKRAINE: THE SIMULATION STUDY

The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of pre-dicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered sta-tistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic pro-cess model in Ukraine based on the polynomial regression method. The constructed model was sufficiently ac-curate in deciding on anti-epidemic measures to combat the COVID-19 pandemic in the selected area. The study of the model in data on the incidence of COVID-19 in Ukraine during the war made it possible to assess the completeness of the recorded statistics, identify the risks of the spread of COVID-19 in wartime, and determine the necessary measures to curb the epidemic curse of the incidence of COVID-19 in Ukraine. The investigation of the experimental study results shows a significant decrease in the registration of the COVID-19 incidence in Ukraine. An analysis of the situation showed difficulty in accessing medical care, a reduction in diagnosis and registration of new cases, and the war led to the intensification of the COVID-19 epidemic process © Dmytro Chumachenko, Pavlo Pyrohov, Ievgen Meniailov, Tetyana Chumachenko, 2022