Ensemble Learned Vaccination Uptake Prediction using Web Search Queries
This work addresses public health planning by predicting vaccination rates, but it is incremental as it applies existing ensemble methods to a new data combination.
The paper tackled predicting future vaccination uptake by combining clinical registry data with web search query frequencies using ensemble learning, achieving a 4.7 Root Mean Squared Error in experiments.
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).