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

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

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

Forecasting of COVID-19 Epidemic Process by Lasso Regression

The coronavirus epidemic has changed the life of the whole world. Containment of the further development of the pandemic requires the implementation of effective evidencebased control measures. For this, it is advisable to use mathematical modeling. The most accurate predictions are shown by machine learning methods. The article discusses a lasso regression model for predicting the dynamics of a new coronavirus in Ukraine, Great Britain, Germany and Japan. The model shows high accuracy. The disadvantage of this approach is the impossibility of identifying the factors influencing the dynamics of morbidity. © 2021 UkrMiCo 2021 - 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, Proceedings. All rights reserved.

Toward Reference Architecture of Control System Socio-Epidemic Processes of Emergent Infections

The pandemic of COVID-19 showed the humanity is vulnerable to threats of epidemic emergent infections. Hence, the challenge of creating a safety system of the population from these threats at territory, national and international levels. The challenge poses a problem in the area of ICT consisting of that developing principles and techniques for engineering flexible decision-making systems. The paper presents a vision of an approach to solving the problem

Machine learning model of covid-19 forecasting in Ukraine based on the linear regression

Coronavirus or COVID-19 is a widespread pandemic that has affected almost all countries around the globe. The quantity of infected cases and deceased patients has been increasing at a fast pace globally. This virus not only is in charge of infecting billions of people but also affecting the economy of almost the whole world drastically. Thus, detailed studies are required to illustrating the following trend of the COVID-19 to develop proper short-term prediction models for forecasting the number of future cases. Generally, forecasting techniques are be inculcated in order to assist in designing better strategies and as well as making productive decisions. The forecasting techniques assess the situations of the past thereby enabling predictions about the situation in the future would be possible. Moreover, these predictions hopefully lead to preparation against potentially possible consequences and threats. It’s crucial to point out that Forecasting techniques play a vital role in drawing accurate predictions. In this research, we categorize forecasting techniques into different types, including stochastic theory mathematical models and data science/machine learning techniques. In this perspective, it is feasible to generate and develop strategic planning in the public health system to prohibit more deceased cases and managing infected cases. Here, some forecast models based on machine learning are introduced and comprising the Linear Regression model which is assessed for time series prediction of confirmed, deaths, and recovered cases in Ukraine and the globe. It turned out that the Linear Regression model is feasible to implement and reliable in illustrating the trend of COVID-19. © 2021 IEEE.

Simulation of COVID-19 Dynamics using Ridge Regression

The COVID-19 pandemic has affected all areas of human activity around the world. Public health systems have shown their unpreparedness for a pandemic of this magnitude. An effective approach to managing the epidemic process is mathematical modeling. In this work, a predictive model of the dynamics of the spread of COVID-19 is built on the basis of the Ridge regression method. The model was verified using data on the incidence of COVID-19 in the UK, Germany, Japan and Ukraine. The choice of these particular countries with different dynamics of the epidemic process makes it possible to adequately investigate the accuracy of the constructed model. © 2021 IEEE.

The concept of decisions support system to mitigate the COVID-19 pandemic consequences based on social and epidemic processes intelligent analysis

The study is aimed at interdisciplinary analysis of social barriers and barriers to overcoming the consequences of epidemics and the development of programs for sociological support of anti-epidemic measures in the context of the COVID-19 pandemic. The goal is to solve the problem of increasing the biosafety of the population as a component of national security through the formation of directions and tools for preparatory work with the public conscience with use of social attitude investigation to ensure the effectiveness of vaccination and minimize the negative non-medical consequences of various measures to combat the COVID-19 pandemic. The concept of comprehensive methodology for analyzing the crisis behavior of the masses with a combination of sociological and mathematical methods has been developed. It is planned to obtain scientifically substantiated information on the social factors of the spread of the virus, the social effects of a sense of hopelessness, social barriers to vaccination and the role of social networks in these processes;a practical task for the project is the development of models of crisis mass behavior and a system of targeted measures for managing the social atmosphere during a prolonged pandemic with uncertain prospects for an exit. It is expected to receive a concept of sociological support for pandemic measures to determine the optimal strategies of media, information and educational and socio-political influence on the state of mass consciousness in the context of the COVID-19 pandemic. © 2021 CEUR-WS. All rights reserved.

Forecasting of COVID-19 epidemic process by support vector machine method in Ukraine and neighboring countries

The global pandemic has affected all areas of life. Scientifically based management decisions to reduce epidemic morbidity not only increase their efficiency, but also save costs aimed at eliminating the virus. For this, mathematical modeling of epidemic processes is used. The most accurate approach to predicting incidence is machine learning. To study and predict the dynamics of the infectious morbidity of COVID-19, a regression model was built based on the support vector machine. The following countries were selected to verify and check the adequacy of the model: Belarus, Hungary, Moldova, Poland, Romania, Russia, Slovakia and Ukraine. Forecasting in these countries allows us to study the impact of the epidemic of neighboring countries on the dynamics in Ukraine, as well as to determine the accuracy of the developed model. © 2021 IEEE.

On-line data processing, simulation and forecasting of the coronavirus disease (COVID-19) propagation in Ukraine based on machine learning approach

The article analyzes the epidemic process of a new coronavirus infection. The influence of COVID-19 on society, education, healthcare and other areas is analyzed. The analysis of restrictive measures that are implemented in different countries is carried out. Based on machine learning methods, a model for the spread of the incidence of COVID-19 has been developed. The forecast of incidence in Ukraine is calculated. The accuracy of the forecast is 97.6%. For automatic calculation of predicted morbidity, a web service has been developed for processing data in real time. The developed model enables to conduct prospective monitoring of the epidemic situation, redistribute limited resources between different regions of the country that need them more at the forecast time, introduce new control methods or, on the contrary, weaken restrictive measures, and adjust measures depending on the forecast situation on the simulated territory. © 2020, Springer Nature Switzerland AG.