COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.

The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges.

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

Topicality. The rapid growth of data in various domains has necessitated the development of efficient tools and libraries for data processing and analysis. Python, a popular programming language for data analysis, offers several libraries, such as NumPy and Numba, for numerical computations. However, there is a lack of comprehensive studies comparing the performance of these libraries across different tasks and data sizes. The aim of the study. This study aims to fill this gap by comparing the performance of Python, NumPy, Numba, and Numba.Cuda across different tasks and data sizes. Additionally, it evaluates the impact of multithreading and GPU utilization on computation speed. Research results. The results indicate that Numba and Numba.Cuda significantly optimizes the performance of Python applications, especially for functions involving loops and array operations. Moreover, GPU and multithreading in Python further enhance computation speed, although with certain limitations and considerations. Conclusion. This study contributes to the field by providing valuable insights into the performance of different Python libraries and the effectiveness of GPU and multithreading in Python, thereby aiding researchers and practitioners in selecting the most suitable tools for their computational needs.