Ensemble Learned Vaccination Uptake Prediction using Web Search Queries

TL;DR

This paper introduces an ensemble learning approach that combines clinical vaccination data and web search query data to accurately predict future vaccination rates, demonstrating the effectiveness of web data alone and the novelty of this application.

Contribution

The study is the first to utilize web search data for vaccination uptake prediction, integrating it with clinical data through ensemble methods for improved accuracy.

Findings

Effective prediction with 4.7 RMSE using combined data

Web data alone achieves comparable performance

First application of web data for vaccination prediction

Abstract

We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official vaccine records show that our method predicts vaccination uptake effectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the first study to predict vaccination uptake using web data (with and without clinical data).