Book Chapter
Estimation of Ukrainian refugees impact on COVID-19 dynamics in Czech Republic using Prophet model
Chumachenko, D., Chomko, V., Meniailov, I., Krivtsov, S., Padalko, H.
In: Chumachenko, D., Kaur, J., Yakovlev, S., Morita, P. (Eds.): Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2023): Waterloo, Canada, November 20-22, 2023, 15–25
CEUR Workshop Proceedings 3641
CEUR-WS.org (2024)
Abstract
This study aims to examine the impact of the Russian invasion of Ukraine on the epidemiological trajectory of COVID-19, demonstrating the interplay between geopolitical instability and infectious disease dynamics. We developed a machine learning model adapted to incorporate the onset of military conflict into its predictive framework. The model's performance was evaluated against actual COVID-19 morbidity and mortality data before and after the invasion, considering a set of identified
epidemiological and conflict-related variables. The predictive accuracy of COVID-19 cases showed a marked decline post-invasion, highlighting the perturbing effect of the conflict on disease spread dynamics. Conversely, the mortality predictions exhibited increased accuracy, suggesting a differential impact of the conflict on various aspects of the epidemic process. The study's findings underscore the necessity of revising epidemiological models in conflict scenarios. It illustrates that incorporating
geopolitical events can significantly alter predictive outcomes and, therefore, should be considered in public health planning and response strategies. The research calls for expanded models that can dynamically integrate various global sociopolitical disturbances and for interdisciplinary efforts to create robust forecasting tools capable of adapting to rapidly changing conditions.