战争对乌克兰新冠肺炎疫情的影响:模拟研究

Q3 Computer Science
D. Chumachenko, Pavlo Pyrohov, I. Meniailov, T. Chumachenko
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引用次数: 4

摘要

COVID-19大流行对全球公共卫生系统构成了挑战。截至2022年3月,全球已报告近5亿例病例。超过620万人死亡。俄罗斯在乌克兰领土上无缘无故发动的战争不仅造成数千人死亡,数十座城市被毁,而且造成了大规模的人道主义危机。军事入侵也影响到公共卫生部门。无法提供医疗服务、在敌对行动频繁的地区不遵守卫生条件、撤离期间人口密度高以及其他因素导致COVID-19在乌克兰的传播进入了一个新阶段。建立适当的疫情过程模型,将有助于评估COVID-19发病率的实际统计数据,并评估遏制疾病流行过程的风险和措施的有效性。本文旨在建立乌克兰COVID-19流行过程的模拟模型,并研究战争条件下实验研究的结果。本研究针对新冠肺炎在军事条件下的流行过程。本研究的主题是基于统计机器学习方法的流行病过程建模的模型和方法。为了达到研究目的,我们采用预测方法,基于多项式回归方法建立了COVID-19流行过程模型。由于这些实验,预测乌克兰30天内新发COVID-19病例的准确性为97.98%,乌克兰COVID-19的死亡率为99.87%。该模型应用于战争第一个月(22年2月24日至22年3月25日)乌克兰COVID-19发病率的数据。计算的预测值与登记的统计量有很大的偏差。结论。本文描述了基于多项式回归方法实现乌克兰COVID-19疫情过程模型的实验研究。所构建的模型对选定地区应对新冠肺炎大流行的防疫措施决策具有足够的准确性。通过对乌克兰战时新冠肺炎发病率数据模型的研究,可以评估记录统计数据的完整性,识别战时新冠肺炎传播的风险,确定遏制乌克兰新冠肺炎发病率流行诅咒的必要措施。对实验研究结果的调查显示,乌克兰新冠肺炎病例登记率明显下降。对局势的分析表明,难以获得医疗服务,新病例的诊断和登记减少,战争导致COVID-19流行病进程加剧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of war on COVID-19 pandemic in Ukraine: the simulation study
The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of predicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered statistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic process model in Ukraine based on the polynomial regression method. The constructed model was sufficiently accurate in deciding on anti-epidemic measures to combat the COVID-19 pandemic in the selected area. The study of the model in data on the incidence of COVID-19 in Ukraine during the war made it possible to assess the completeness of the recorded statistics, identify the risks of the spread of COVID-19 in wartime, and determine the necessary measures to curb the epidemic curse of the incidence of COVID-19 in Ukraine. The investigation of the experimental study results shows a significant decrease in the registration of the COVID-19 incidence in Ukraine. An analysis of the situation showed difficulty in accessing medical care, a reduction in diagnosis and registration of new cases, and the war led to the intensification of the COVID-19 epidemic process.
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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
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2 weeks
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