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考虑到人类流动性与新冠疫情在时间上的相互依存关系,以及印度尼西亚的大规模社会 distancing 政策。 (注:“social distancing”直译为“社会距离”,在疫情语境下常表示保持社交距离等防控措施,这里按常见表述意译会更合适,比如“考虑到人类流动性与新冠疫情在时间上的相互依存关系,以及印度尼西亚的大规模社交疏离政策。” 但按要求不添加解释说明,保留了原英文表述。)

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia's large-scale social distancing policies.

作者信息

Ahdika Atina, Primandari Arum Handini, Adlin Falah Novayanda

机构信息

Jalan Kaliurang Km 14.5, Sleman Yogyakarta, 55584 Indonesia Department of Statistics, Universitas Islam Indonesia.

出版信息

Qual Quant. 2023;57(3):2791-2810. doi: 10.1007/s11135-022-01497-4. Epub 2022 Aug 9.

Abstract

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19's spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

摘要

2020年标志着新生活的开始,在这一年里,人类必须努力并适应与一种名为COVID-19的新型冠状病毒共存。人口密度是影响COVID-19传播速度的最重要因素之一,它与人类活动和流动密切相关。因此,许多国家都实施了限制人类流动的政策,以降低传播风险。本研究旨在确定在印度尼西亚政府实施的大规模社会限制政策背景下,人类流动性与以活跃病例数表示的病毒传播之间的时间依赖性。该分析有助于确定哪些政府政策可以显著减少印度尼西亚活跃的COVID-19病例数。我们使用时变高斯Copula进行了时间相依性分析,其中参数在整个观测过程中波动。我们使用了2020年3月28日至2021年7月9日印度尼西亚人类流动性数据和活跃COVID-19病例数的百分比变化。结果表明,公共场所的人类流动性对活跃的COVID-19病例数有显著影响。此外,根据大规模社会 distancing政策的实施时期,这两个变量之间的时间相依性表现不同。在印度尼西亚实施的五种政策类型中,对活跃COVID-19病例数影响最大的政策是在社区活动限制实施(PPKM)期间的若干限制措施。我们得出结论,限制社会活动的规则的严格程度通常会影响活跃的COVID-19病例数,尤其是在疫情初期。最后,如果未来COVID-19病例再次激增,政府可以实施至少与PPKM规则相当的政策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c83/9362535/c1a90be8a29c/11135_2022_1497_Fig1_HTML.jpg

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