ABSTRACT
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
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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
ABSTRACT
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
ABSTRACT
The new coronavirus has changed the life of the planet and continues to spread around the world. Mathematical modeling allows the development of effective scientifically substantiated preventive and anti-epidemic measures. Machine learning methods have the highest accuracy when constructing the predicted incidence of infectious diseases. In this work, a model of a random forest was built to calculate the predicted incidence of COVID-19. To verify the model, data on the incidence of coronavirus in Ukraine, Great Britain, Germany and Japan were used. These countries were chosen because have different dynamics of the epidemic process and different control measures. © 2021 IEEE.
ABSTRACT
An outbreak of a new coronavirus infection was first recorded in Wuhan, China, in December 2019. On January 30, 2020, the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on March 11, it a pandemic. As of January 2022, over 340 million cases have been reported worldwide;more than 5.5 million deaths have been confirmed, making the COVID-19 pandemic one of the deadliest in history. The digitalization of all spheres of society makes it possible to use mathematical and simulation modeling to study the development of the virus. Building adequate models of the epidemic process will make it possible not only to predict its dynamics but also to conduct experimental studies to identify factors affecting the development of a pandemic, determine the behavior of the virus in certain areas, assess the effectiveness of measures aimed at stopping the spread of infection, as well as assess the resources needed to counter the epidemic growth of the disease. This study aims to develop three regression models of the COVID-19 epidemic process in given territories and to investigate the experimental results of the simulation. The research is targeted at the COVID-19 epidemic process. The research subjects are methods and models of epidemic process simulation, which include machine learning methods, particularly linear regression, Ridge regression, and Lasso regression. To achieve the research aim, we have used forecasting methods and have built the COVID-19 epidemic process and regression models. As a result of experiments with the developed model, the predictive dynamics of the epidemic process of COVID-19 in Ukraine, Germany, Japan, and South Korea for 3, 7, 10, 14, 21, and 30 days were obtained. The authorities making decisions on the implementation of anti-epidemic measures can use such predictions to solve the problems of operational analysis of the epidemic situation, an analysis of the effectiveness of already implemented anti-epidemic measures, medium-term planning of resources needed to combat the pandemic, etc. Conclusions. This paper describes experimental research on implementing three regression models of the COVID-19 epidemic process. These are models of linear regression, Ridge regression, and Lasso regression. COVID-19 daily new cases statistics were verified by these models for Ukraine, Germany, Japan, and South Korea, provided by the Johns Hopkins Coronavirus Resource Center. All built models have sufficient accuracy to make decisions on the implementation of anti-epidemic measures to combat the COVID-19 pandemic in the selected area. Depending on the forecast period, regression models can be used to solve different Public Health tasks. © 2022. Dmytro Chumachenko, Ievgen Meniailov, Kseniia Bazilevych, Olha Chub. All Rights Reserved.
ABSTRACT
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.
ABSTRACT
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
ABSTRACT
The substantial ascendant trend within the number of daily infected new cases with coronavirus around the world is a warning, and several other researchers are utilizing various mathematical and machine learning-based prediction models to forecast the long-term trend of the COVID-19 pandemic. During this research, the Autoregressive Integrated Moving Average or ARIMA model was implemented to forecast the COVID-19 expected daily number of cases in Ukraine. We implemented Autoregressive Integrated Moving Average for this research. The forecasting results showed that the trend in Ukraine will continue ascending and should reach up to more than 1.8 million total cases if stringent precautionary and control measures don’t get implemented to prohibit the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
The article presents an approach to modeling epidemic processes based on machine learning. A model is built based on the polynomial regression method. The simulation results allow us to calculate the predicted incidence of coronavirus infection in a certain area. The model has been shown to be accurate enough for use in public health policy-making settings. The disadvantage of using machine learning methods is the impossibility of identifying factors affecting the dynamics of the epidemic process. But, due to their high accuracy, such models can be used in an ensemble with agent-based and compartment models. © 2021 CEUR-WS. All rights reserved.
ABSTRACT
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.
ABSTRACT
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. © 2021. A. Mohammadi, I. Meniailov, K. Bazilevych, S. Yakovlev, D. Chumachenko, 2021
ABSTRACT
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.
ABSTRACT
The use of methods for predicting epidemic processes and mathematical modeling of the dynamics of morbidity allows the development and implementation of scientifically based methods for the prevention and containment of the epidemic spread of an infectious disease. The study focuses on the development and implementation of the linear Holt model for predicting the incidence of COVID-19 in Ukraine. The advantage of the method is its high accuracy for short-term forecasting for 10 days. The disadvantage of this method is the impossibility of identifying factors that affect the behavior of the epidemic process. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)