Vaccination intentions generate racial disparities in the societal persistence of COVID-19

Abstract

We combined survey, mobility, and infections data in greater Boston, MA to simulate the effects of racial disparities in the inclination to become vaccinated on continued infection rates and the attainment of herd immunity. The simulation projected marked inequities, with communities of color experiencing infection rates 3 times higher than predominantly White communities and reaching herd immunity 45 days later on average. Persuasion of individuals uncertain about vaccination was crucial to preventing the worst inequities but could only narrow them so far because 1/5th of Black and Latinx individuals said that they would never vaccinate. The results point to a need for well-crafted, compassionate messaging that reaches out to those most resistant to the vaccine.

Open Debates Conducive for Vaccination Rate Flatlines: A Scoping Review and Convergent Cross Mapping

“…Some scientists endorsed the official rhetoric ( 19 – 23 ). Supporters concluded that the more virulent Delta variant posed a risk to the “pandemic of the unvaccinated” before the eventual attainment of herd immunity ( 24 ), particularly in-person schools ( 25 ), and Black, Indigenous, and People of Color (BIPOC) ( 26 ). Dr. Hooper strongly urged that healthcare workers (HCWs) should get their jabs in her editorial ( 27 ), as well as Kelly and Jackson ( 28 ).…”

Understanding and addressing public health disparities through the lens of human mobility data

“…Additionally, this method could apply to datasets in other countries or regions during different pandemics to investigate the local virus diffusion pattern. The method could also be used to investigate the neighborhood-level virus spreading and, following our prior research [67], provide a new perspective to understand the racial disparities among communities. and many other nations began to distribute vaccines to high-risk groups shortly thereafter and then extended distribution to the general public in the spring.…”

“…Moreover, it has become a widely used source to understand various aspects of human mobility behaviors, and their applications extend to customer behavior analysis [12,13], urban planning [14,15], disaster recovery [16,17,18], public health [19,20,21,5,22] and beyond.…”

“…New insights into human movements have been revealed through findings based on these data [8,9,10,11]. Moreover, it has become a widely used source to understand various aspects of human mobility behaviors and their applications extend to urban planning [14,15], pandemic modeling [19,20,5,21,22], disaster recovery [16,17,18], and beyond. Therefore, these emerging big data are increasingly viewed as a promising alternative that can supplement or even replace the traditional, probabilistically collected survey data [6].…”