Mobility, COVID-19 cases and virus reproduction rate data analysis for Romania using Machine Learning Algorithms

被引:2
|
作者
Dobrita, Gabriela [1 ]
Bara, Adela [1 ]
Oprea, Simona-Vasilica [1 ]
Baroiu, Costin [1 ]
Barbu, Dragos-Catalin [1 ]
机构
[1] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest, Romania
来源
2022 26TH INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC) | 2022年
关键词
COVID-19; mobility flows; data mining; K-means; RandomForest Regressor;
D O I
10.1109/ICSTCC55426.2022.9931806
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper examines the effect of lockdown measures on population mobility during the COVID-19 pandemic in Romania. Many countries, including Romania, have adopted quarantine and isolation for infected people in attempting to stop the virus transmission and the rapid escalation of the number of cases, which has put great pressure on hospitals. These measures have slowed down the economy leading to the loss of jobs with a massive impact on people's psychology by increasing panic or anxiety. Mobility data is used as a measurement representation of social distancing and therefore, combining mobility data sets with COVID-19-related data might support the analysis between the virus effects and mobility and, correspondingly, the population mobility impact on virus transmission. It is undeniable that the imposed restrictions have influenced the business environment, our aim being to investigate the relationship between the number of COVID-19 cases, the virus reproduction rate, and the changes in mobility toward retail outlets and workplaces.
引用
收藏
页码:244 / 251
页数:8
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