  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.
