Кафедра епідеміології
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Item 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 ОлександрівнаItem Artificial intelligence solutions for global health and disaster response: Challenges and opportunities(2024-09) Chumachenko, Dmytro; Чумаченко, Дмитро; Morita, Plinio Pelegrini; Ghaffarian, Saman; Chumachenko, Tetyana; Чумаченко, Тетяна ОлександрівнаItem Editorial: Artificial intelligence solutions for global health and disaster response: challenges and opportunities(2024-09) Chumachenko, Dmytro; Чумаченко, Дмитро; Morita, Plinio Pelegrini; Ghaffarian, Saman; Chumachenko, Tetyana; Чумаченко, Тетяна ОлександрівнаItem A Comparison of Ukrainian Hospital Services and Functions Before and During the Russia-UkraineWar JAMA Health Forum(2024) Haque, Ubydul; Bukhari, Moeen Hamid; Fiedler, Nancy; Wang, Shanshan; Korzh, Oleksii; Espinoza, Juan; Miraj Ahmad; Holovanova, Irina; Chumachenko, Tetyana; Marchak, Olga; Chumachenko, Dmytro; Ulvi, Osman; Sikder, fthekarl; Hubenko, Hanna; Barrett, Emily S.Item Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models(2023) Chumachenko, Dmytro; Чумаченко, Дмитро Ігорович; Dudkina, Tetiana; Дудкіна, Тетяна; Yakovlev, Sergiy; Яковлев, Сергій; Chumachenko, Tetyana; Чумаченко, Тетяна ОлександрівнаItem 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 ОлександрівнаItem A Prognostic Model and Pre-Discharge Predictors of Post-COVID-19 Syndrome After Hospitalization for SARS-CoV-2 Infection(Frontiers Media S.A., 2023-11) Honchar, Oleksii; Ashcheulova, Tetiana; Гончарь, Олексій Володимирович; Ащеулова, Тетяна Вадимівна; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Chumachenko, Dmytro; Чумаченко, Дмитро Ігоревич; Bobeiko, Alla; Бобейко, Алла Євгенівна; Khodosh, Eduard; Ходош, Едуард Михайлович; Blazhko, Viktor; Блажко, Віктор Іванович; Matiash, Nataliia; Матяш, Наталія Михайлівна; Ambrosova, Tetiana; Амбросова, Тетяна Миколаївна; Herasymchuk, Nina; Герасимчук, Ніна Миколаївна; Kochubiei, Oksana; Кочубєй, Оксана Анатоліївна; Smyrnova, Viktoriia; Смирнова, Вікторія ІванівнаBackground. Post-COVID-19 syndrome (PCS) has been increasingly recognized as an emerging problem: 50% of patients report ongoing symptoms 1 year after acute infection, with most typical manifestations (fatigue, dyspnea, psychiatric and neurological symptoms) having potentially debilitating effect. Early identification of high-risk candidates for PCS development would facilitate the optimal use of resources directed to rehabilitation of COVID-19 convalescents. Objective. To study the in-hospital clinical characteristics of COVID-19 survivors presenting with self-reported PCS at 3 months and to identify the early predictors of its development. Methods. 221 hospitalized COVID-19 patients underwent symptoms assessment, 6-minute walk test, and echocardiography pre-discharge and at 1 month; presence of PCS was assessed 3 months after discharge. Unsupervised machine learning was used to build a SANN-based binary classification model of PCS development. Results. PCS at 3 months has been detected in 75% patients. Higher symptoms level in the PCS group was not associated with worse physical functional recovery or significant echocardiographic changes. Despite identification of a set of pre-discharge predictors, inclusion of parameters obtained at 1 month proved necessary to obtain a high accuracy model of PCS development, with inputs list including age, sex, inhospital levels of CRP, eGFR and need for oxygen supplementation, and level of post-exertional symptoms at 1 month after discharge (fatigue and dyspnea in 6MWT and MRC Dyspnea score). Conclusions. Hospitalized COVID-19 survivors at 3 months were characterized by 75% prevalence of PCS, the development of which could be predicted with an 89% accuracy using the derived neural network-based classification model.Item Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion’s Effect on Italy’s COVID-19 Dynamics(2023-11-04) Chumachenko, Dmytro; Чумаченко, Дмитро; Dudkina, Tetiana; Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Plinio Pelegrini, MoritaItem 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 ОлександрівнаItem Simulation of epidemic processes: a review of modern methods, models and approaches(2022) Chumachenko, Tetyana; Чумаченко, Тетяна Олександрівна; Chumachenko, DmytroThe paper is devoted to an overview of the current state of research on the modeling of epidemic processes. The classification of mathematical and simulation models of epidemic processes is carried out. The disadvantages of classical models are revealed. Specific characteristics inherent in epidemic processes have been determined, which must be taken into account when constructing mathematical and simulation models. A review of deterministic compartment models is carried out. Various methods and approaches to the construction of statistical models of epidemic processes are considered. The types of problems that are solved using machine learning are analyzed.