分析 COVID-19 大流行期间日本每日旅行距离的因素。

IF 2.6 4区 工程技术 Q1 Mathematics
Masaya Mori, Yuto Omae, Yohei Kakimoto, Makoto Sasaki, Jun Toyotani
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引用次数: 0

摘要

人们普遍认为,COVID-19 大流行病的全球影响是一个重大问题,而人流在其传播过程中起着至关重要的作用。因此,近期研究的重点是确定和分析能够有效调节人流的因素。然而,在预计会产生影响的多种因素中,很少有研究调查 COVID-19 大流行期间与人流特别相关的因素。此外,很少有研究调查这些因素的地区特征和疫苗接种数量如何影响人流。此外,在报告不足的国家和地区增加核实病例的数量对于归纳结论也很重要。因此,本研究使用基于机器学习的人流预测模型,对日本千叶县楢野市进行了群体层面的分析。根据地区特征和接种疫苗的数量对高重要性群体进行了细分,并在因子水平上进行了可视化和相关性分析。研究结果表明,基于树的模型,尤其是 LightGBM,在预测方面表现更好。此外,接种疫苗的累计人数和新感染人数也可能是人流变化的解释因素。分析表明,在疫苗接种尚未开始时,日本或东京的新感染者人数比其居住地区的新感染者人数更有流动趋势。随着疫苗接种的实施,对其居住地区新感染人数的关注可能会增加。然而,在疫苗接种普及后,对感染风险的感知可能会降低。这些发现有助于提出有效控制人流的新措施,并确定何时减轻或加强具体措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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