ArticleHuman vaccines & immunotherapeutics2022
Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.
Article in Human vaccines & immunotherapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- How can timeliness and accuracy be balanced? The surveillance information reporting mechanism for public health emergencies based on the online direct reporting system.BMC public health · 2025Article
- Article
- Predicting mobile health clinic utilization for COVID-19 vaccination in South Carolina: A statistical framework for strategic resource allocation.PLOS global public health · 2025Article
- Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets.JMIR infodemiology · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods.Computational biology and chemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.