{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T18:50:22Z","timestamp":1769971822498,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health of Ukraine","award":["0123U100184"],"award-info":[{"award-number":["0123U100184"]}]},{"name":"Ministry of Health of Ukraine","award":["RGPIN-2017-05310"],"award-info":[{"award-number":["RGPIN-2017-05310"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["0123U100184"],"award-info":[{"award-number":["0123U100184"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["RGPIN-2017-05310"],"award-info":[{"award-number":["RGPIN-2017-05310"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, leading to healthcare disruptions, medical resource shortages, and heightened emergency care needs. Italy emerged as a significant refuge for displaced Ukrainians during this period. Aim: This research aims to discern the impact of the Russian full-scale invasion of Ukraine on the COVID-19 transmission dynamics in Italy. Materials and Methods: The study employed advanced simulation methodologies, particularly those integrating machine learning, to model the pandemic\u2019s trajectory. The XGBoost algorithm was adopted to construct a predictive model for the COVID-19 epidemic trajectory in Italy. Results: The model demonstrated a commendable accuracy of 86.03% in forecasting new COVID-19 cases in Italy over 30 days and an impressive 96.29% accuracy in estimating fatalities. When applied to the initial 30 days following the escalation of the conflict (24 February 2022, to 25 March 2022), the model\u2019s projections suggested that the influx of Ukrainian refugees into Italy did not significantly alter the country\u2019s COVID-19 epidemic course. Discussion: While simulation methodologies have been pivotal in the pandemic response, their accuracy is intrinsically linked to data quality, assumptions, and modeling techniques. Enhancing these methodologies can further their applicability in future public health emergencies. The findings from the model underscore that external geopolitical events, such as the mass migration from Ukraine, did not play a determinative role in Italy\u2019s COVID-19 epidemic dynamics during the study period. Conclusion: The research provides empirical evidence negating a substantial influence of the Ukrainian refugee influx due to the Russian full-scale invasion on the COVID-19 epidemic trajectory in Italy. The robust performance of the developed model affirms its potential value in public health analyses.<\/jats:p>","DOI":"10.3390\/computation11110221","type":"journal-article","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T14:51:01Z","timestamp":1699109461000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion\u2019s Effect on Italy\u2019s COVID-19 Dynamics"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2623-3294","authenticated-orcid":false,"given":"Dmytro","family":"Chumachenko","sequence":"first","affiliation":[{"name":"Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 61070 Kharkiv, Ukraine"},{"name":"School of Public Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"given":"Tetiana","family":"Dudkina","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 61070 Kharkiv, Ukraine"}]},{"given":"Tetyana","family":"Chumachenko","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Kharkiv National Medical University, 61000 Kharkiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9515-6478","authenticated-orcid":false,"given":"Plinio Pelegrini","family":"Morita","sequence":"additional","affiliation":[{"name":"School of Public Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON M5G 1A1, Canada"},{"name":"Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON M5S 1A1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s41579-020-00459-7","article-title":"Characteristics of SARS-CoV-2 and COVID-19","volume":"19","author":"Hu","year":"2020","journal-title":"Nat. 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