Accepted for/Published in: JMIR Infodemiology
Date Submitted: Oct 20, 2022
Date Accepted: Jun 26, 2023
Date Submitted to PubMed: Jun 30, 2023
Using COVID-19 vaccine attitudes on Twitter to improve vaccine uptake forecast models in the United States: Infodemiology Study of Tweets Abstract
ABSTRACT
Background:
Since the onset of the COVID-19 pandemic, there has been a global effort to develop vaccines that protect against COVID-19. Individuals who are fully vaccinated are far less likely to contract and therefore transmit the virus to others. Researchers have found that the internet and social media both play a role in shaping personal choices about vaccinations.
Objective:
The present study aims to determine whether supplementing COVID-19 vaccine uptake forecast models with the attitudes found in tweets improves over baseline models that only use historical vaccination data.
Methods:
Daily COVID-19 vaccination data at the county-level was collected for the January 2021 to May 2021 study period. Twitter’s streaming API was used to collect COVID-19 vaccine tweets during this same period. Several ARIMA models were executed to predict the vaccine uptake rate using only historical data (baseline ARIMA) and individual Twitter-derived features (ARIMAX).
Results:
In this study, we found that supplementing baseline forecast models with both historical vaccination data and COVID-19 vaccine attitudes found in tweets reduced RMSE by as much as 83%.
Conclusions:
Developing a predictive tool for vaccination uptake in the United States will empower public health researchers and decision makers to design targeted vaccination campaigns in hopes of achieving the vaccination threshold required for the United States to reach widespread population protection.
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Copyright
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