Crossing Borders, Luxembourg Institute for Socio-Economic Research (LISER), Esch-sur-Alzette, Luxembourg.
Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
Global Health. 2022 Apr 18;18(1):41. doi: 10.1186/s12992-022-00832-6.
Assessing the impact of government responses to Covid-19 is crucial to contain the pandemic and improve preparedness for future crises. We investigate here the impact of non-pharmaceutical interventions (NPIs) and infection threats on the daily evolution of cross-border movements of people during the Covid-19 pandemic. We use a unique database on Facebook users' mobility, and rely on regression and machine learning models to identify the role of infection threats and containment policies. Permutation techniques allow us to compare the impact and predictive power of these two categories of variables.
In contrast with studies on within-border mobility, our models point to a stronger importance of containment policies in explaining changes in cross-border traffic as compared with international travel bans and fears of being infected. The latter are proxied by the numbers of Covid-19 cases and deaths at destination. Although the ranking among coercive policies varies across modelling techniques, containment measures in the destination country (such as cancelling of events, restrictions on internal movements and public gatherings), and school closures in the origin country (influencing parental leaves) have the strongest impacts on cross-border movements.
While descriptive in nature, our findings have policy-relevant implications. Cross-border movements of people predominantly consist of labor commuting flows and business travels. These economic and essential flows are marginally influenced by the fear of infection and international travel bans. They are mostly governed by the stringency of internal containment policies and the ability to travel.
评估政府对新冠疫情的应对措施的影响对于控制疫情和为未来的危机做好准备至关重要。我们在此研究非药物干预措施(NPIs)和感染威胁对新冠大流行期间人员跨境流动的日常演变的影响。我们使用了一个关于脸书用户流动性的独特数据库,并依靠回归和机器学习模型来确定感染威胁和遏制政策的作用。排列技术允许我们比较这两类变量的影响和预测能力。
与国内流动的研究不同,我们的模型指出,在解释跨境交通变化方面,遏制政策比国际旅行禁令和对感染的恐惧更为重要。后者由目的地的新冠病例和死亡人数来代理。尽管强制政策在不同的建模技术中的排名不同,但目的地国家的遏制措施(如取消活动、限制内部流动和公众集会)和原籍国的学校关闭(影响父母休假)对跨境流动的影响最大。
虽然我们的研究结果具有描述性,但它们具有政策相关性。人员的跨境流动主要由劳动力通勤流和商务旅行组成。这种经济和必要的流动受到感染恐惧和国际旅行禁令的影响较小。它们主要受内部遏制政策的严格程度和旅行能力的影响。