通过空间监测减少对 COVID-19 疫苗的犹豫不决

IF 2.8 3区 地球科学 Q1 GEOGRAPHY
Ge Lin, Tonglin Zhang
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引用次数: 0

摘要

阿瑟-格蒂斯是空间统计学的先驱。他与基思-奥德的合作启发了我们地理学家与统计学家之间的长期合作。盖蒂斯经常利用空间统计学解决现实世界中的传染病问题,这不时激励着我们的工作。在本文中,我们报告了一项为期 10 周的空间干预措施,以减少 COVID-19 疫苗的犹豫不决。与时空建模不同,我们结合社会脆弱性指数(SVI)绘制并检测了每周疫苗接种的空间模式。在第一周至第八周期间,我们发现了 COVID-19 疫苗接种的大量空间集群效应。这些效应与 SVI 呈负相关,这意味着更脆弱的人群接种疫苗的可能性更小。这一方向性效应转变为正效应,表明干预措施取得了显著进展。尽管我们在最初几周观察到了一些全球性的空间集群,但在控制了 SVI 后,低值集群或疫苗犹豫的冷点已不复存在。使用空间统计和 SVI 可以帮助监测有针对性的干预措施,以减少疫苗接种差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spatial monitoring to reduce COVID-19 vaccine hesitance

Spatial monitoring to reduce COVID-19 vaccine hesitance

Arthur Getis is a pioneer in spatial statistics. His collaboration with Keith Ord has inspired our long-standing collaboration between a geographer and a statistician. Getis often tackled real-world infectious disease problems using spatial statistics, which has motivated our work from time to time. In this paper, we report a 10-week spatial intervention for reducing COVID-19 vaccine hesitancy. In contrast to spatiotemporal modeling, we mapped and detected spatial patterning of vaccination each week in conjunction with the social vulnerability index (SVI). Between week one and week eight, we identified substantial spatial clustering effects of COVID-19 vaccine administrations. These effects were negatively associated with SVI, meaning that the more vulnerable populations were less likely to be vaccinated. This directional effect changed to positive suggesting significant progress from the intervention. Even though we observed some global spatial clustering in the early weeks, low-value clusters or cool spots for vaccine hesitance were no longer present after SVI was controlled. The use of spatial statistics and the SVI can help monitor targeted interventions to reduce vaccination disparities.

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来源期刊
CiteScore
5.40
自引率
6.90%
发文量
33
期刊介绍: The Journal of Geographical Systems (JGS) is an interdisciplinary peer-reviewed academic journal that aims to encourage and promote high-quality scholarship on new theoretical or empirical results, models and methods in the social sciences. It solicits original papers with a spatial dimension that can be of interest to social scientists. Coverage includes regional science, economic geography, spatial economics, regional and urban economics, GIScience and GeoComputation, big data and machine learning. Spatial analysis, spatial econometrics and statistics are strongly represented. One of the distinctive features of the journal is its concern for the interface between modeling, statistical techniques and spatial issues in a wide spectrum of related fields. An important goal of the journal is to encourage a spatial perspective in the social sciences that emphasizes geographical space as a relevant dimension to our understanding of socio-economic phenomena. Contributions should be of high-quality, be technically well-crafted, make a substantial contribution to the subject and contain a spatial dimension. The journal also aims to publish, review and survey articles that make recent theoretical and methodological developments more readily accessible to the audience of the journal. All papers of this journal have undergone rigorous double-blind peer-review, based on initial editor screening and with at least two peer reviewers. Officially cited as J Geogr Syst
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