评估俄罗斯在乌克兰的战争对COVID-19在西班牙传播的影响:一项基于机器学习的研究

Q3 Computer Science Radioelectronic and Computer Systems Pub Date : 2023-03-07 DOI:10.32620/reks.2023.1.01
D. Chumachenko, T. Dudkina, T. Chumachenko
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引用次数: 1

摘要

COVID-19大流行对世界产生了重大影响,数百万人感染和死亡,医疗保健系统不堪重负,经济中断,日常生活发生变化。模拟已被认为是防治这一流行病的宝贵工具,有助于模拟病毒的传播,评估干预措施的影响,并为决策过程提供信息。模拟的准确性和有效性取决于基础数据、假设和建模技术的质量。正在进行的改进和完善模拟方法的努力可以提高它们在处理未来突发公共卫生事件方面的价值。俄罗斯于2022年2月24日全面军事入侵乌克兰,造成了严重的人道主义和公共卫生危机,医疗服务中断,医疗用品短缺,对紧急护理的需求增加。持续的冲突使数百万人流离失所,西班牙从乌克兰登记的难民数量在世界上排名第五。该研究旨在利用机器学习的手段估计俄罗斯在乌克兰的战争对COVID-19在西班牙传播的影响。本研究以战争期间新冠肺炎疫情为研究对象。研究课题是基于机器学习的流行病过程仿真方法和模型。为了达到研究目的,我们采用预测方法,基于XGBoost方法建立了COVID-19流行过程模型。实验结果表明,西班牙30天新发病例预测准确率为99.79%,西班牙新冠肺炎死亡病例预测准确率为99.86%。该模型应用于战争升级前30天(2022年2月24日至2022年3月25日)西班牙COVID-19发病率的数据。计算的预测值表明,俄罗斯全面入侵导致的乌克兰人口被迫迁移到西班牙,并不是影响西班牙COVID-19流行过程动态的决定性因素。结论。本文描述了一项实验性研究的结果,该研究评估了俄罗斯在乌克兰的全面战争对西班牙COVID-19动态的影响。该模型在公共卫生实践中取得了良好的应用效果。对实验研究所得结果的分析表明,俄罗斯全面入侵导致的乌克兰人口被迫迁移到西班牙,并不是影响西班牙COVID-19流行过程动态的决定性因素。
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Assessing the impact of the russian war in Ukraine on COVID-19 transmission in Spain: a machine learning-based study
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 military 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 epidemic 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 analysis of the obtained results of the experimental study 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.
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来源期刊
Radioelectronic and Computer Systems
Radioelectronic and Computer Systems Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
3.60
自引率
0.00%
发文量
50
审稿时长
2 weeks
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