Spatial monitoring to reduce COVID-19 vaccine hesitance

Ge Lin () and Tonglin Zhang ()
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Ge Lin: Hong Kong University of Science & Technology (Guangzhou)
Tonglin Zhang: Purdue University

Journal of Geographical Systems, 2024, vol. 26, issue 2, No 6, 249-264

Abstract: Abstract 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.

Keywords: Vaccine hesitance; Quasi-Poisson model; Social vulnerable index; Spatial clustering and clusters; Spatial scan statistic; Overdispersion (search for similar items in EconPapers)
JEL-codes: C12 C21 I18 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10109-023-00437-6

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