Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: May 1, 2022
Date Accepted: Dec 22, 2022
Date Submitted to PubMed: Jan 10, 2023

The final, peer-reviewed published version of this preprint can be found here:

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis

Li Q, Peng J, Mohan D, Lake B, Ruiz A, Weir B, kan L, Yang C, Labrique A

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis

JMIR Public Health Surveill 2023;9:e39166

DOI: 10.2196/39166

PMID: 36626835

PMCID: 9937108

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: A spatiotemporal big data analysis

  • Qingfeng Li; 
  • James Peng; 
  • Diwakar Mohan; 
  • Brennan Lake; 
  • Alex Ruiz; 
  • Brian Weir; 
  • Lena kan; 
  • Cui Yang; 
  • Alain Labrique

ABSTRACT

Background:

Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns (“It’s Up To You”) to communicate vaccine information and encourage timely vaccination across the US.

Objective:

The goal of this study was to utilize aggregated mobility data to assess the effectiveness of the campaign on COVID vaccine uptake.

Methods:

Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and de-identified mobile devices across the country. A Bayesian spatio-temporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (age, race/ethnicity, income, and political affiliation). We also incorporated Conditionally Autoregressive (CAR) residual models to account for apparent spatio-temporal autocorrelation.

Results:

The dataset covers a panel of 3,104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 – August 29, 2021). Officially launched in February, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which started the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0 - 2.4%) on a weekly basis, compared to the baseline case of no campaign.

Conclusions:

The “It’s Up To You” campaign is effective in promoting COVID vaccine uptake, suggesting that a nationwide targeted mass media campaign with multi-sectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics, and that mobile-phone mobility based methods can be effective in measuring impact in near-real-time. Clinical Trial: NA


 Citation

Please cite as:

Li Q, Peng J, Mohan D, Lake B, Ruiz A, Weir B, kan L, Yang C, Labrique A

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis

JMIR Public Health Surveill 2023;9:e39166

DOI: 10.2196/39166

PMID: 36626835

PMCID: 9937108

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.