Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries

TL;DR

This paper introduces a novel adaptive method for estimating vaccination uptake from web search data that accounts for irregular temporal patterns, outperforming existing approaches especially during periods of fluctuation.

Contribution

It presents the first adaptive approach to vaccination uptake estimation from web data, removing the assumption of regular temporal features in prior models.

Findings

Our method outperforms state-of-the-art baselines in accuracy.

Performance gains are significant for vaccines with irregular uptake patterns.

The approach is especially effective during periods of media attention or supply issues.

Abstract

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).