Кафедра епідеміології

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Subject: search.filters.subject.epidemic process

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Now showing 1 - 10 of 22
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    Methodology for assessing the impact of emergencies on the spread of infectious diseases
    (2024) Chumachenko, Dmytro; Чумаченко, Дмитро Ігорович; Bazilevych, Kseniia; Базілевич, Ксенія Олексіївна; Butkevych, Mykola; Буткевич, Микола Віталійович; Meniailov, Ievgen; Меняйлов, Євген Сергійович; Parfeniuk, Yurii; Парфенюк, Юрій; Sidenko, Ievgen; Сіденко, Євген Вікторович; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна
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    The Framework of Estimation of the Impact of the Russian War on the Infectious Diseases Spreading
    (2023) Chumachenko, Dmytro; Чумаченко, Дмитро; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна
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    Challenges in interpretation of epidemic process simulation models results
    (2023-06-28) Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна
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    Assessing the impact of the Russian war in Ukraine on COVID-19 transmission in Spain: a machine learning-based study
    (2023) Chumachenko, Dmytro; Pudkina, Tetiana; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна
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    Multiagent simulation model of the COVID-19 epidemic process with social factors
    (2022-10-30) Salun, Olga; Chumachenko, Dmytro; Muradyan, Olena; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна
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    Impact of war on COVID-19 pandemic in Ukraine: the simulation study
    (2022) Chumachenko, Dmytro; Pyrohov, Pavlo; Meniailov, Ievgen; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Чумаченко, Татьяна Александровна
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    Barriers of COVID-19 vaccination in Ukraine during the war: the simulation study using arima model
    (2022) Chumachenko, Dmytro; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Чумаченко, Татьяна Александровна; Kirinovych, Nataliia; Meniailov, Ievgen; Muradyan, Olena; Salun, Olga
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    Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting
    (2022-05-30) Chumachenko, Dmytro; Meniailov, Ievgen; Bazilevych, Kseniia; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Чумаченко, Татьяна Александровна; Yakovlev, Sergey
    COVID-19 has become the largest pandemic in recent history to sweep the world. This study is dev oted 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.
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    Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach
    (MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations, 2022-04-23) Chumachenko, Dmytro; Piletskiy, Pavlo; Sukhorukova, Marya; Chumachenko, Tetyana
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    On intelligent agent-based simulation of COVID-19 epidemic process in Ukraine
    (2022) Chumachenko, Dmytro; Meniailov, Ievgen; Bazilevych, Kseniia; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Чумаченко, Татьяна Александровна; Yakovlev, Sergiy
    COVID-19 has impacted all areas of human activity around the world. Modern society has not faced such a challenge. Affordable travel and flights between continents allowed the virus to rapidly spread to all corners of the world. An effective tool for the development of anti-epidemic measures is mathematical modeling. The paper proposes a simulation model of COVID-19 propagation based on an agent-based approach. The case of the spread of the epidemic process before vaccination is considered. To verify the model, we used the data of official statistics on the incidence of COVID-19 in Ukraine, provided by the Center for Public Health of the Ministry of Health of Ukraine. The constructed model makes it possible to identify the factors influencing the development of the COVID-19 epidemic in a certain area. Посилання на статтю https://doi.org/10.1016/j.procs.2021.12.310