Forecasting the COVID-19 vaccine uptake rate: An infodemiological study in the US

TL;DR

This study presents a novel framework combining clinical data and web search queries using advanced regression and machine learning techniques to accurately forecast COVID-19 vaccine uptake in the US, aiding policy decisions.

Contribution

It introduces a stacking regression model that integrates ARIMA and machine learning methods for improved vaccine uptake prediction.

Findings

Stacked regression outperforms individual models in accuracy.

ARIMA (1,0,8) best models clinical data.

Boosted support vector machine best models web search data.

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.