Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries
This work addresses the challenge of accurately monitoring public health vaccination rates using real-time web data, which is incremental as it builds on prior web-based estimation methods by adding temporal adaptability.
The paper tackles the problem of estimating vaccination uptake by introducing a method that dynamically adapts to temporal fluctuations in web search data, removing the assumption of temporal regularity used in prior work. It shows that this method outperforms state-of-the-art baselines, with performance improvements particularly pronounced for vaccines with irregular uptake due to factors like negative media attention or supply issues.
Estimating vaccination uptake is an integral part of ensuring public health. It was recently shown that vaccination uptake can be estimated automatically from web data, instead of slowly collected clinical records or population surveys. All prior work in this area assumes that features of vaccination uptake collected from the web are temporally regular. We present the first ever method to remove this assumption from vaccination uptake estimation: our method dynamically adapts to temporal fluctuations in time series web data used to estimate vaccination uptake. We show our method to outperform the state of the art compared to competitive baselines that use not only web data but also curated clinical data. This performance improvement is more pronounced for vaccines whose uptake has been irregular due to negative media attention (HPV-1 and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of 12 years old (whose vaccination is more irregular compared to younger children).