Zhou, X;
Li, Y;
(2022)
Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.
Human Vaccines and Immunotherapeutics
, 18
(1)
, Article 2017216. 10.1080/21645515.2021.2017216.
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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.
| Type: | Article |
|---|---|
| Title: | Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/21645515.2021.2017216 |
| Publisher version: | https://doi.org/10.1080/21645515.2021.2017216 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
| Keywords: | Public health, forecast, infodemiology, machine-learning, vaccine, COVID-19, COVID-19 Vaccines, Disease Outbreaks, Forecasting, Humans, Models, Statistical |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10156911 |
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